During the college admissions process, students are presented with an overload of information from each school they are applying to and accepted by. A critical aspect for deciding on a school is the estimated Cost of Attendance (COA) and the financial aid package. Each school calculates their COA differently and thus offers a unique financial aid package. It is important for students to have a way of comparing and evaluating a school's cost with financial aid. While college counselors have developed excel sheets with algorithms that compare personalized cost with financial aid and scholarships, not all students are familiar with excel which may result in an inaccurate analysis. Transparent Tuition is a tool for students to accurately compare financial aid options from each university they are applying to. This project was developed using React.js and Spring Boot. These are two development libraries that will make Transparent Tuition scalable in the future. By creating a user-friendly web tool, students can better understand the school’s information and make a more educated decision when deciding on their university. Students will be able to connect with a college counselor to receive advice regarding their options when choosing a university. This will allow students to make an educated decision on their college based on both the short-term and long-term financial impact.

College students go through many transitions during their time at school. They learn to live on their own, manage household tasks, and balance their academics. A specific change in college is to learn how to grocery shop and cook for yourself. When students move off campus, they go from a dining plan where most of their meals are provided to needing to make all their meals. This results in many students relying on fast food or the same easy meals. Easy Bites, in partnership with TCU’s Nutrition Department, is designed to help students find quick, cheap, and nutritious meals. All our recipes are designed by Nutrition students on campus for college students to add variety to their diet. Easy Bites is composed of two aspects: an online portal for nutrition students to submit recipes for approval, and a mobile app for college students to view recipes. Our mobile app is connected to the Kroger database to provide users with accurate information about specific ingredients prices and availability. By working with the Nutrition Department and connecting with the Kroger database, we are making it easier for students from the deciding on recipes, shopping for the ingredients, and making the meal. With this, Easy Bites makes it easier to make nutritious meals as a college student.

49 million people in the United States have suffered from anxiety disorder in the past year, and 80 million have suffered in their lifetime. Many traditional methods of treatment, while often helpful, are sometimes inaccessible, time-consuming, expensive, intimidating, or overall impractical. In a world where people are increasingly in need of care and therapists are increasingly burnt out, technology bridges the gap and increases accessibility for those who previously would have been excluded. What CognitV strives to create as a solution is a Virtual Reality Exposure Therapy experience where patients can face their anxiety in a safe, controlled environment through a VR headset. Geared towards players with Social Anxiety Disorder, this treatment method allows patients to safely expose themselves to public speaking and confrontational scenarios from the comfort and privacy of their own homes. This treatment method would be faster and more accessible, is preferred by younger patients, and fills the treatment avoidance gap, all while providing a realistic, immersive experience that can effectively aid in treating mental health disorders, either with or without an accompanying clinician.

Using Virtual Reality and Artificial Intelligence, CognitV creates an immersive environment geared towards Players with Social Anxiety Disorder which allows them to safely expose themselves to public speaking and confrontational scenarios from the comfort and privacy of their own homes.

“AI Powered Patent Analysis Software”
Patent AI is an online patent analysis tool which gives feedback on uploaded patent application documents and provides a likelihood of it being accepted by the USPTO.
This tool is meant to reduce the rate of rejected patents –being at 90%– and the wait time associated in getting a response from the USPTO.
Our application is informational, accurate, intuitive, and will simplify the patent application process.

IMPROVING COLLECTION MANAGEMENT IN THE MONNIG METEORITE COLLECTION.
R. G. Mayne, J. Huther, Y. Tyagi, B. Danysh, R. Perkins, M. O' Connor, T. Truong, and B. Wei.
Monnig Meteorite Collection, Texas Christian University, 2950 W Bowie Street, Fort Worth, TX 76109 (r.g.mayne@tcu.edu)
Department of Computer Science, Texas Christian University, Suite 341, 2840 W Bowie Street, Fort Worth, TX 76109.

Introduction: Collection Management Software is a vital tool in sample-based science and a key part of any scientific collection of meteorites. However, this software is often designed as a one-size-fits-all solution, which can be used for all collections within a museum. As a result, much of the commercially available software for collections management is not ideal for the curation needs of extraterrestrial materials. Platforms are often vendor-specific, contain redundant and unnecessary functionality, and require significant time to be invested in staff training.

Over the past two decades, The Monnig Meteorite Collection has utilized FileMaker Pro for the management of the Collection. FileMaker Pro was chosen as it allows the user to design a custom solution to fit their specifications. However, this either requires that the administrator stays current on all updates and functionality of the software, or continual investment in external support for the system. The current database was designed in 2014 and is no longer meeting the needs of the Monnig Collection or the users of the database, who are primarily sample-based scientists and collectors. After consultation with industry experts, curators, and users of the database, it was decided that an update of the current database was not the best approach for the Collection, instead a new custom database that meets the needs of both the Curator and the user was commissioned.
This project introduces the development of a comprehensive database and user-friendly web application portal, marking a substantial improvement over the existing legacy system.

Project Overview: The primary aim of the Monnig Meteorite Database Project, hereafter referred to as MMDP, is to offer a detailed and robust database for the Monnig Meteorite Collection. It will feature an enhanced catalog search portal, enabling users to explore and search the collection through various parameters and filters. The system is also designed to aid gallery curators and administrators by providing detailed views of collection items, tracking sample history, and managing loans, all within a secure and user-friendly interface.

MMDP seeks to preserve the wealth of knowledge encompassed within the Monnig Meteorite Collection. The digital database and search tool will facilitate research and offer broad access to the collection for researchers, collectors, educators, and students. This initiative is set to serve as a valuable educational and scientific resource, equipped with extensive functionalities.

The database is being developed as a senior design project in the Department of Computer Science at Texas Christian University (TCU). The senior design project is a year-long program required of all Computer Science and Data Science graduates, where they work with external clients to develop and implement workable solutions to the briefs provided.

System Development and Preparation: in the Fall 2023 semester, the MMDP Team focused on data preparation and outlining the project scope into needs (must have features for launch), wants (features that are not required at launch but the capability to add them later is required), and wishes (features that are not required). Inconsistencies in the legacy data were identified and corrected; these included repeated entries, varied date formats, typographic errors, and missing fields. Python was utilized for data cleaning, and the team standardized data and organized it into relational database tables using PostgreSQL, hosted on Azure cloud for maintenance and backup.

Development will continue throughout the Spring 2024 semester and the outdated and insecure legacy portal will be replaced with a newly developed web application. This application is being built using Spring Boot for backend operations, and HTML5, CSS, and the VueJS Framework for a responsive front-end UI, ensuring accessibility across various devices. The current launch date for the new collections management system is May 2024.

Functionalities: MMDP will address the need for functionality for both the administrators of the database (primarily the Curator in this case) and the external user (Figure 1). The required parameters for both of these audiences are described below.
All users of the database will be able to:
1. perform parameterized searches using criteria such as Name, Monnig Number, Class, Group, Clan, Country, and Observed Fall or Found (Figure 2a).
2. filter and modify search results directly on the search result page (Figure 2b).
3. Find accessible detailed information about each meteorite sample, including images, from the search results via individual display pages for each sample.
4. download all the search results based on the given constraints with a single click from the search results page.

Administrators will be able to
1. have access to specialized functionalities that are secured and restricted from regular users. Upon logging in, they are presented with a portal offering various database management options.
2. view more detailed information about samples than regular users, including the sample's history and loan information. They have the ability to add new meteorite samples or create subsamples.
3. perform data manipulation tasks, such as deleting or modifying existing sample records.
4. have control over the media associated with samples, allowing them to add or delete media.
5. administrators able to create, view, update, and delete history entries for each sample. This historical data management is a key new feature not possible in the current system.
6. Access loan management capabilities include adding, modifying, archiving, and accessing archived loan entries for samples.
7. print labels for samples, which can be used for curation in the vault.

Summary: The MMCD stands as a model of integration, combining domain expertise, data best practices, and user-centric design. This project offers a template for other universities, museums, galleries, and research centers aiming to enhance their functionalities and provide a seamless, user-friendly experience for accessing and managing meteorite data collections.
Embodying the spirit of scientific collaboration, this initiative is open to opportunities for collaboration to expand the platform's capabilities or to implement similar solutions in other institutions.

Acknowledgments: We are grateful to the Department of Computer Science at TCU for their continued support of the Monnig Meteorite Collection through the Senior Design project. We also thank Dustin Dickens for his advice and assistance in the discovery portion of the database redesign.

The Department of Computer Science at Texas Christian University offers a course where senior students, in teams, collaborate with clients to solve real-world software problems. Students handle every project phase: definition, analysis, design, implementation, testing, deployment, and documentation. However, in these teams, there's a variation in how much each student contributes. Some are very active, while others are not. Communication issues can also arise. To handle these challenges and improve team efficiency, there is a Student Performance Tracking system in place that includes Weekly peer evaluations where each student evaluates their own teammates in accordance with the rubric defined by the professor and Weekly Activity Reports (WAR) where each student writes down their own contributions for the week.

While this system works and improves team efficiency, these tools are too manual and thus time consuming. For the WAR, each student has to edit the Google Docs document for the week which is then reviewed by the professor. This can lead to human error, meaning some students might not get the right credit if they make mistakes while filling out the Google Docs document. For the Peer Evaluation, each student must review the WAR for the week and then make an excel spreadsheet to evaluate their teammates and then upload it to TCU Online. Once all students have turned in their peer evaluation report for the week, the professor has to download reports of all students and then run these through a Java program which then calculates the results for all students. Then the professor uploads the results to TCU Online (a course management tool used by TCU). Not only does this leave room for human error on the students' side (spreadsheets must have the right columns), but it is also very time consuming for the professor as they have to download all reports manually from TCU Online and then run the Java program and finally upload the results back to TCU Online.

The automated Student Performance Tracking system (Peer Evaluation Tool) streamlines the evaluation process by providing a centralized website where students can directly fill out their Weekly Activity Reports (WARs) and complete peer evaluations. It also enables them to view their own submitted WARs and received peer evaluation scores from their teammates. For the instructor, the system offers the functionality to create and customize evaluation rubrics, which ensures consistency in peer assessments. Instructors can access and review all peer evaluations and WARs, allowing them to monitor team dynamics and individual contributions efficiently. This comprehensive solution eliminates the manual handling of documents and the need for external spreadsheet software, thereby reducing human error and saving time for both students and instructors.

This presentation investigates the learning process of artificial intelligence by training a model to play the game of Go using an AlphaZero-type algorithm. Through evaluation of 12 Go models, the authors reveal the split personality many exhibit, much like the famous Schreiber book Sybil. The best models appear indistinguishable from human players in the early stages of the game before devolving into self-destructive tendencies in the endgame. Possible remedies for this behavior are explored through modifying training data generation, hyperparameter tuning, and optimizing neural network input dimensions.

In the dynamic environment of venues with large seating capacities, efficient management of seating occupancy emerges as a critical challenge. Traditional manual monitoring methods are often cumbersome and prone to inaccuracies, hindering optimal seat allocation and event management. Addressing this issue, our senior design project introduces an AI-based solution tailored to revolutionize real-time seating availability reporting for event organizers.
This project aims to provide a comprehensive tool that enables event organizers to track seating occupancy in real-time, facilitating the identification of peak attendance periods and enabling data-driven decision-making. By harnessing the power of artificial intelligence, our system offers a detailed analysis of seating patterns, thereby enhancing the efficiency of event operations and optimizing resource allocation. The ultimate goal is to improve the event experience for both organizers and attendees by ensuring a seamless flow of information regarding seating availability, leading to more effective management of large-scale events. Through this initiative, we endeavor to set a new standard in venue management, where technology and data converge to create smarter, more responsive event environments.

Efficient teacher-student interaction analysis is essential for educators to enhance teaching quality. Traditional manual review methods are excessively time-consuming and can yield subpar feedback. ClassifAI offers a streamlined solution for educators to gain insights without sacrificing work hours, utilizing the OpenAI Whisper model for transcription and a fine-tuned Gemma model for question categorization.

ClassifAI is advancing existing tools by addressing four key improvements: transitioning to local hosting for cost savings and data security, integrating the WhisperX model for improved transcription accuracy, automating Costa's Three Levels of Thinking question classification via Google's Gemma, and upgrading the web interface for better user experience.

ClassifAI's architecture comprises a user-friendly web server with ExpressJS and React, a local MongoDB database, a fine-tuned Gemma model for question categorization, and WhisperX for speech-to-text. ClassifAI offers speech recognition, diarization, question categorization, and analysis, delivering enhanced performance. Educators easily upload their teaching audio/video on our platform via a file or YouTube, which is then processed by our GPU server for transcription and analysis. The resulting transcript, graphs, and metrics are accessible for review and can be exported in various formats.

Speech impairment ranks among the world's most prevalent disabilities, affecting over 430 million adults. Despite its widespread impact, many existing video-conferencing applications lack a comprehensive end-to-end solution for this challenge. In response, we present a holistic approach to translate American Sign Language to subtitles in real time by leveraging advancements in Google Mediapipe, Transformer models, and web technologies. In March 2024, Google released the largest dataset for the problem domain with over 180 GB in size, containing ASL gesture sequences represented as Mediapipe numeric values. Our methodology begins with the implementation and training of a Transformer model using preprocessed Google dataset, followed by the establishment of a back-end server equipped with the trained model. This server handles video input preprocessing and real-time inference, communicating with client services as a REST endpoint. To demonstrate the practicality of our approach, we developed a video conferencing application utilizing the AgoraRTC SDK, which communicates with our back-end server to transcribe user gestures to text in real time, displaying them on the receiving end. Through this end-to-end system, we enable video calls enhanced by the real-time transcription of fingerspelled gestures with low latency and high accuracy, effectively bridging the communication gap for individuals with speech disabilities. With a growing imperative for AI applications engineered for human well-being, our project seeks to promote the integration of AI in applications designed to enhance human wellness, thus bringing the broader awareness and adoption of this endeavor.

Chalk Mountain Services of Texas, LLC. is a trucking company whose business is transporting raw materials, such as fracking sand, to various oilfield sites in and around west Texas. With over 1,300 assets in their fleet, they’re presented with a number of logistical problems, like optimizing a driver’s time to make as many trips between drill sites and raw material depots as possible in a day. Such routing and scheduling applications must have accurate data—the assets are either in or out of service and their location—to schedule sensible routes.

Should an asset break down in the unforgiving terrain of west Texas, the appropriate employee should have the ability to take note of such an incident so that routing and scheduling applications have correct, up-to-date data. The company’s current solution allows for any user to make changes to any asset, regardless of authorization status. Inconsistencies in assets’ statuses can lead to an employee having to manually intervene in the scheduling process, which decreases the company’s overall efficiency. Additionally, their current application is not mobile-friendly, but a sizable portion of users nevertheless interface with the current website from their phones.

The company’s expectations come in either one of two forms: a website and a companion app or a reactive website that can be used on a desktop or mobile device. The application shall use CRUD—create, read, update, and delete—methods to keep track of the assets, and the application shall provide different users with different access levels with Active Directory authentication. We have created a reactive website that can be used from either a desktop environment or mobile one, and our implementation of their requirements exists as a three layer architecture: a Microsoft SQL Server database, a backend developed in NodeJS, and a React front end. To make the deployment as simple as possible, we did not pursue developing the application on cloud providers; the application depends on a connection to an in-house SQL server and Active Directory service both of which cannot be accessed outside their intranet and are critical to the application’s functionality.

The Instructional Equity Observing Tool is an online video/audio analysis tool that is geared towards assisting the teachers and faculty of educational institutions in analyzing and understanding how their interaction with students translates into real learning. Our platform is meant to replace the current, manual method of analysis that many teachers/instructors perform to try and quantify different metrics about their teacher-student interaction. Instructors have expressed desire to view metrics such as the time the teacher talks during a lesson, what is the response time of students to those questions, and other data points such as the types of questions being asked (as categorized by Bloom’s Taxonomy). Quantifying these instructional variables helps these instructors more accurately understand the areas that they are strong in, and more importantly, the areas in which they can be more interactive with the students as to allow them to better absorb the lessons being taught. With the help of our tool, we can allow teachers to quickly and efficiently gather this data about each of their lessons so that data driven changes in teaching techniques is possible, and moreover, so that teachers can identify potential vectors of ineffective instruction.
The process for using this application is for a user to login/sign-up for our site, then they will proceed to upload either an audio or video file to the designated location. Our tool will then take that video/audio file and execute a customized API call to AssemblyAI (https://www.assemblyai.com/) that transcribes this file into text. We then perform specialized data manipulation operations on the transcript to generate all the different metrics and display them in an easy-to-read format that the user can then scroll through and analyze the results. The user will also have the option to save this report that is generated as a pdf, which they or an administrator role will be able to access and view again at a later time.
Our application is hosted using Amazon Web Services (AWS) and utilizes many different functionalities that this service provides. AWS manages our authentication and authorization, user account management, and report storage functionalities. Our current system does not use its own machine learning model and instead offloads transcription to the AssemblyAI API, however this could be updated in the future with the addition of large datasets for training. A specifically trained machine learning model in this case could provide a more accurate categorization of questions and a more flexible tool that could eventually make predictions or suggestions to the user on the best ways to improve their teaching methods.

Open Planner is a web application designed to meet the increasing need for college students to have a way to more easily organize and access major
assignment/exam dates across all courses during busy college semesters. Open Planner seeks to ease agenda making for students by parsing uploaded student syllabi for major assignment/exam dates and generate a personalized calendar the student can access from his/her account upon sign-up and syllabus upload. Once they have access to their personal calendar, students will be able to add events, delete and modify existing events, and customize their course calendars, giving them fast access to a customized and modifiable calendar without the time demanding task of looking through course syllabi and adding major dates one by one.

Indigenous communities have a deep-seated understanding of the importance and sacredness that their land has in their daily lives (native lands.ca); they have a deep sense of place. The primary objective of Native Meteorites (NaMe) is to amplify the work of the Native Earth | Native Sky (NENS) program by recognizing the critical importance of free-choice learning in STEM education and providing a different lens through which STEM can be made culturally relevant for students in Native American nations.
This project focuses specifically on meteorites found on the lands of the three Oklahoma Native American tribes participating in NENS and provides a concrete example of the cultural relevance of planetary science and STEM, utilizing concepts that are deeply rooted in a sense of place. The goal of this project is to increase the interest and participation of an underrepresented important people group in the national STEM workforce, as well as provide an example of the relevance of place-based STEM education for all individuals.
This project consists of an interactive map, which displays where relevant meteorites landed; and also provides supplementary resources for education. Members of the NaMe project will develop STEM resources that focus on meteorites found on Native American Lands. This will be unlike other free-choice learning because this interactive map caters specifically to indigenous peoples’ learning styles.
In collaboration with Native American individuals, the team designed the site layout, content, and imagery to be as inclusive and considerate as possible. The product of this project ultimately caters to an audience that is quite underrepresented– so we used conscious software development in the website-building process.
The interactive map feature of this site will increase the interest and participation of an underrepresented important people group in the national STEM workforce, as well as provide an example of the relevance of place-based STEM education for all individuals.

The system to be is BMW Performance Horse Database, also referred to as BMWPHD. The client is Brooke Wharton with BMW Quarter Horses. The purpose of her company is to breed and raise horses for reining and reined competitions. Currently this field faces the issue that horse data is spread over multiple different platforms that do not communicate with one another. With that, the main objective of BMWPHD is to create a user-friendly searchable database for the task of finding and ranking horses for breeding, buying, and determining show schedules. The users of this application include fans, riders, coaches, judges, and investors in the sport. The hope is to not only bring more fans to the sport through the easy access to data, but also improve the level of competition so that the horses can be bred stronger and therefore perform at a higher level within the sport. On the technical side, the system will be implemented with the following technologies: the frontend will use Vue.js, the backend will be implemented in Java Spring Boot, the database will be built in PostgreSQL. The final version of the application will be deployed on Heroku.

The Chinese Learning Platform(CPL) is a program to help students to learn the Chinese language. This platform will be used by both students of these ages attempting to learn Chinese as well as by the teachers who will use the platform as a teaching tool to help those students. As it is a teaching tool, the main motivation behind it is educational, with the hope to support students in learning the Chinese language, and in the future, this will be expanded to learning various other languages using the same CPL. The platform hopes to help these students utilize a textbook created by CPL, and will also include features that will help the students listen, read, write, and speak in the language they are learning.

The COVID-19 pandemic has made it difficult for families to stay connected, especially those separated by distance. Keepsake is a software product that was developed with the aim of helping families bridge the gap by enabling them to share stories and memories across generations. The platform provides a secure and private space where family members can record and post audio content that can be accessed by their loved ones anytime, anywhere via cloud storage.

Keepsake offers an intuitive user interface that is accessible to users of all ages, making it easy for them to navigate and listen to the audio content. By hosting the platform on Amazon Web Services (AWS), Keepsake provides a reliable and scalable solution for storing and retrieving audio files/posts across the years. The platform is designed to ensure that each family's audio files are separate and private from other family audio files, offering complete privacy to users.

To get started with Keepsake, users can easily join their families and start recording and uploading audio files. The platform allows for organization and sharing with specific family groups, making it easy to share stories and memories with those who matter most. Keepsake is a powerful tool for connecting families across generations, providing accessibility, convenience, and security for families of all sizes and backgrounds.

Our product seeks to provide a teacher-driven computer programming education platform that allows users total anonymity in communication and grading. The purpose of this software is to provide educators the ability to assign students both in class programming contests that are graded on a time-to-completion basis and to facilitate both guided and collaborative communication about programming and computer software. This product was initially designed to be used in university’s Intro to Programming classes where the professor recognized that students, especially females, were hesitant to participate due to a perceived lack of knowledge of the topic. In any situation, asking questions can be beneficial, and this platform will provide students the ability to ask their peers and professors questions without the fear of negative reflection on their knowledge or understanding. 
The Platform is built on a custom serverless architecture utilizing Amazon Web Services (AWS). The Platform hosts a publicly accessible web portal, API layers for integration and data manipulation, and database and object storage solutions for data management and storage. Our choice in using AWS gave us the ability to implement pre-built and managed security solutions for our project. The security of our users information is offloaded immediately to a managed AWS service to minimize potential penetrations.
During the course of the project, we enhanced our time management skills and learnt how to collaborate and communicate within a team. Ultimately the research project will be considered a success if the application promotes better communication and learning within the classroom.

Day traders typically spend most of their day looking at graphs to try to find specific patterns and changes in the market. The chance of making a rewarding investment could be gone while traders try to figure out whether the pattern is good or bad. This tedious and time-consuming job can be made easier and quicker. Our team members, David Greenwell, Alfredo Perez, Zhengwei Zhou, Ahn Nguyen, and Kyle Conte have been working hard to build an algorithm to find one of the best possible market patterns called the three-bar pattern. This three-bar pattern is a pattern one might see in the market, and it shows a turning point in the market. Our client Dr. Zhang, a day trader, was interested in a way to find this pattern in real-time, on a select few stocks. With the help of Dr. Ma, Dr. Wei, and Dr. Kadiyala, our sponsors, we have created the algorithm and are working on implementing a web application for it.

Toyota Financial Services (TFS), being part of a highly regulated industry needs to ensure that all risk management, governance process, and controls are in place to ensure compliance. This entails documenting all the business processes, definitions of their data elements, connecting the defined data elements to the physical attributes in their various applications and databases. Furthermore, they need to document the lineage of the data to ensure that it is flowing correctly through their ecosystem. In addition to these, they must ensure the data quality at the source and through the transformations, it goes through while flowing in their ecosystem.
The problem of the disjointed system to record, store, check and correct all the data in the ecosystem/ no holistic view of data is affecting the employers/ business partners of TFS, the impact of which is unorganized data, manual process of linking physical and business data elements which is time-consuming.
A solution that our team is working towards is to build a data portal where data will be organized by business areas such as Loan Originations, Insurance, Servicing, etc., and various classifications under those areas. We are also implementing a google-like search for any data element which would bring up business definitions, physical attributes, data quality rules, profiles, and any related data associated with it.

Toyota Financial Services (TFS) is the largest automotive lender in the nation with over $125B in total assets. TFS offers financing, leasing, protection plans, and other financial services to customers and dealers all across the United States. In order to serve their customers better, it was required to have a comprehensive view of the customer. The TFS Customer 360 team has worked with TFS to create a Customer 360 view by harnessing the power of graph databases, semantic queries, and graph visualization tools. This view represents all direct and indirect relationships that exist for a customer and will be made available to different stakeholders in the company to make more informed decisions and to better identify potential opportunities.

The Monnig comprises a scientific research collection (the Monnig Meteorite Collection) as well as a Museum (the so-called “Gallery”). The exhibit has an educational game and interactive video screens. While there have been some updates to the technology in the exhibits in the last twenty years, most of the Gallery remains unchanged since its opening in 2003.

The current design is not inclusive for visually impaired visitors. contents are not accessible for non-English speaking visitors, and only less than 5 % of the 3000 meteorite collections are displayed. We address these problems by developing a tour assistance application with sufficient accommodations for visually impaired visitors using Android tablets that will be provided by the MMG to its incoming visitors with a capability of being fine-tuned to the individual’s preference.

The application begins with three separate menus, each with its own screen, which allows the user to customize the app to their needs. Menu 1 allows them to select their language. The beta version includes English, Spanish, and French, but later versions could consist of more language options. Menu 2 provides a font size selection, and menu 3 allows users to identify their color blindness type. These variables can be reset or changed at any time. The Monnig Meteorite Collection database has images of all of the meteorites within the Gallery and the text from each exhibit will be reproduced within the application. The images will be shown on a high contrast background (as compared to the exhibits) to allow for better viewing of the samples. Voice transcription will also eventually be available. Wayfinding, the ability for the user to identify where they are within the Gallery, on the application will be achieved in one of two ways. QR codes will be placed on each display case, allowing users to scan and locate the relevant exhibit on the map when needed. In addition, Bluetooth receivers will be used so that the application can identify where in the Gallery the user is located.

Chinese Learning Platform is a part of STARTALK Program – a federal grant program funded by the National Security Agency. The mission of the program is to assist students in learning languages identified critical by STARTALK, including Chinese, Arabic, Hindi, Persian, Korean, Russian, and Turkish. Our project aims to support students in learning Chinese, and will be extended to other languages in a near future. The supports include, but are not limited to, assistance in vocabulary, listening, reading, writing, and speaking Chinese. In addition, our project contains well-designed functionalities dedicated to language learning, thus further improving the learning experience for students.

The TCU and UNTHSC School of Medicine requires its students to participate in service learning with various non-profit partner organizations in the community. Our team's goal is to make the volunteer sign-up process easier and more convenient for med students, to automate the tracking of students' hours, and to ease the burden on faculty in charge of managing the entire process. We aim to accomplish these goals with a web application that will streamline the volunteer scheduling and hour-tracking process for students and faculty.

The TCU Computer Science Department has launched an AlphaGo research project. Currently, it can only be used by those directly involved with the project, and only at certain computers on campus. In addition, the interface for conducting research is difficult to use. Our goal is to make this project more widely accessible to students and faculty alike, whether they wish to help in research, or simply want to learn to play Go. We have developed a web application for the project that allows users to play against various Go AI agents, as well as allowing researchers to train new AI. In addition, our site allows various admin functions to control and edit users and AI agents alike.