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North Carolina is labeled as the 10th hungriest state in America, with almost 1 in 5 children in North Carolina facing hunger regularly. Based on these numbers alone, childhood hunger is an important issue that needs to be addressed. This paper focuses on the thirty-four counties serviced by the Food Bank of Central and Eastern North Carolina. Each county is mapped out, showing the percentage of children receiving free or reduced meals. These numbers are then compared to the number of Weekend Power Packs, Kid Cafes, and School Pantry locations per county. This project illustrates the strength of visual analytics for decision making 

University food pantries have been opened on-campus to reduce food insecurity among students. However, they are relatively self-governed and receive a limited amount of support. This study aims to resolve the inventory management issue at university food pantries using the combination of the Model-View-Controller (MVC) software pattern and data visualization. As a result, a foundation is established to predict the food choices of clients and to manage food waste effectively. The benefits of visualization on decision making through the use of a resourceful inventory system are outlined. The focus of this research is on university pantries, in particular, the Aggie Source Food Pantry at North Carolina Agricultural & Technical State University. A sample of 50 clients' food choices was acquired from picklists for June and July of 2019. The inventory tracking system implemented is a client-server mobile application used for data collection. Data visualization was applied to evaluate the food donations and distributions. Students preferred essential foods (e.g., pasta, canned vegetables), over unhealthy foods (e.g., Pop-Tarts, cookies). The data consisted of 338 pounds of distributed food and almost 2,473 pounds of donations. Data was simplified into comprehensive visual diagrams. The MVC structure established a program interface that grouped application functions and managed data objects. The solution allowed the staff to understand pantry status and the trends in production. It is anticipated that the prototype will be implemented in the daily activities of the pantry. 

In the ever-changing world of computer security and user authentication, the username/password standard is becoming increasingly outdated. Using the same username and password across multiple accounts and websites leaves a user open to vulnerabilities, and the need to remember multiple usernames and passwords feels very unnecessary in the current digital age. Authentication methods of the future need to be reliable and fast, while maintaining the ability to provide secure access. Augmenting traditional username-password standard with face biometric is proposed in the literature to enhance the user authentication. However, this technique still needs an extensive evaluation study to show how reliable and effective it will be under different settings. Local Binary Pattern (LBP) is a discrete yet powerful texture classification scheme, which works particularly well with image classification for facial recognition. The system proposed here strives to examine and test various LBP configurations to determine their image classification accuracy. The most favorable configurations of LBP should be examined as a potential way to augment the current username and password standard by increasing their security with facial biometrics. 

Community Food Strategies provides guidance and tools to local food networks in North Carolina to empower the creation of equitable policy change at all levels. Their vision is the realization of an equitable food system that improves the quality of life for all. In Davidson County, the recently formed Local Food Network, created under the guidance of Community Food Strategies, is working to create a more equitable and sustainable food system and is evaluating how best to apply their resources toward this end. The county is largely rural, and rural areas tend to have lower food access. Two cities, Lexington and Thomasville, have experienced economic depression over the past three decades with the exodus of furniture manufacturing overseas and as such, both cities have communities that suffer from poverty and food insecurity issues. In this research, we use visual analytics and spatial analysis to build a survey and analysis of local food availability for the county. Visual analytics is increasingly being applied to applications like this to increase understanding of large datasets and improve decision making. This research provides a base analysis for the Local Food Network to present the state of the local food system to potential partners who can help create policies that will increase equity and availability of food to everyone in the county. 

We are creating a streamlined way to adapt WebIDs [1], and biometrics [2] to the cyber world. This involves building a user authentication system that enables quick, fast and secure access. It is understood that compared to traditional username and password user authentication, WebIDs are designed to provide such services. Nevertheless, if an intruder either has direct access to the user's computer or somehow gets the unique certificate of the user, important information can be stolen with solely the use of WebIDs. Since biometric data (e.g. fingerprints, iris scanning, etc.) is unique and not easily duplicated, this possibility can be avoided by including biometrics in the authentication process. We also include an enrollment protocol that checks whether a user has a WebID while trying to access a server. If they do, we allow the user access to the server, and if they do not, by accessing their own server, we register the user for a WebID with their permission. Implementing these features in the WebID protocol will greatly enhance user authentication safety.