Matching Items (56)

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Dining Hall Web Application

Description

For my thesis project, I have developed a cash register web application for the Arizona State University Barrett Dining Hall. I previously worked at the Barrett Dining Hall, and I would occasionally step in as a cashier. This work is

For my thesis project, I have developed a cash register web application for the Arizona State University Barrett Dining Hall. I previously worked at the Barrett Dining Hall, and I would occasionally step in as a cashier. This work is how I came to be familiar with the system and all its inefficiencies. The system requires multiple user inputs to implement even the most basic of tasks, is not user-friendly, and therefore very prone to error. In the event that multiple incorrect inputs are entered, the software will freeze, and the user will have to turn off the computer and turn it back on. In theory, this application is an improvement over the software system that is currently in place in that the user interface has been specifically designed to be user-friendly. This application reduces the number of required user inputs by automating certain tasks (such as pricing and determining the meal period), thereby reducing the chance of user error. It is also an improvement in that it allows students to log in to the system to view how many meals they have left, how much M&G is in their account, and how many guest passes they have left. This functionality is extremely important because this is a feature that is not currently in place, and is something that students have actively complained about. Currently, if students want to check on their meal plan, they have to either physically go to a dining hall and ask the cashier, or call a toll-free number. The two technologies used to develop this application are C# and XML. These technologies were chosen because I wanted to learn something new for this project to broaden my knowledge. I also happened to be taking a class at the start of this project that utilized C# and XML for Web Applications, and it seemed like the perfect opportunity to transfer over the skills I had been learning.

Contributors

Created

Date Created
2018-05

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Task Organizer Platform for Class and Group Collaboration

Description

There exist many very effective calendar platforms out there, from Google Calendar, to Microsoft’s Outlook, and various implementations by other service providers. While all those services serve their purpose, they may be missing in the capacity to be easily portable

There exist many very effective calendar platforms out there, from Google Calendar, to Microsoft’s Outlook, and various implementations by other service providers. While all those services serve their purpose, they may be missing in the capacity to be easily portable for some, or the capacity to offer to the user a ranking of their various events and tasks in order of priority. This is that, while some of these services do offer reliable support for portability on smaller devices, it could be even more beneficial to the user to constantly have an idea of which calendar entry they should prioritize at a given point in time, based on the necessities of each entry and regardless of which entry occurs first on a chronologic line. Many of these capacities are missing in the technology currently used at ASU for course management. This project attempts to address this issue by providing a Software Application that offers to store a user’s calendar events and present those events back to the user after arranging them by order of priority. The project makes use of technologies such as Fibrease, Angular and Android to make the service available through a web browser as well as an Android mobile client. We explore possible avenues of implementations to make the services of this platform accessible and usable through other existing platforms such as Blackboard or Canvas. We also consider ways to incorporate this software into the already existing workflow of other web platforms such as Google Calendar, Blackboard or Canvas, by allowing one platform to be aware of any item creation or update from the other platform, and thus removing the necessity of creating one calendar entry multiple times in different platforms.

Contributors

Agent

Created

Date Created
2019-05

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Honey, I Forgot the Milk: An Alexa Shopping Assistant

Description

If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may

If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.

Contributors

Agent

Created

Date Created
2019-05

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Alexa Discussion Board Skill

Description

A common challenge faced by students is that they often have questions about course material that they cannot ask during lecture time. There are many ways for students to have these questions answered, such as office hours and online discussion

A common challenge faced by students is that they often have questions about course material that they cannot ask during lecture time. There are many ways for students to have these questions answered, such as office hours and online discussion boards. However, office hours may be at inconvenient times or locations, and online discussion boards are difficult to navigate and may be inactive. The purpose of this project was to create an Alexa skill that allows users to ask their Alexa-equipped device a question concerning their course material and to receive an answer retrieved from discussion board data. User questions are mapped to discussion board posts by use of the cosine similarity algorithm. In this algorithm, posts from the discussion board and the user’s question are converted into mathematical vectors, with each term in the vector corresponding to a word. The values of these terms are computed based on the word’s frequency within the vector’s corresponding document, the frequency of that word within all the documents, and the length of the document. After the question and candidate posts are converted into vectors, the algorithm determines the post most similar to the user’s question by computing the angle between the vectors. With the most similar discussion board post determined, the user receives the replies to the post, if any, as their answer. Users are able to indicate to their Alexa device whether they were satisfied by the answer, and if they were unsatisfied then they are given the opportunity to either rephrase their question or to have the question sent to a database of unanswered questions. The professor can view and answer the questions in this database on a website hosted by use of Amazon’s Simple Storage Service. The Alexa skill does well at answering questions that have already been asked in the discussion board. However, the skill depends heavily on the user’s word choice. Two questions that are semantically identical but different in phrasing are often given different answers. This is because the cosine algorithm measures similarity on the basis of word overlap, not semantic meaning, and thus the application never truly “understands” what type of answer the user desires. Improving the performance of this Alexa skill will require a more advanced question answering algorithm, but the limitations of Amazon Web Services as a development platform make implementing such an algorithm difficult. Nevertheless, this project has created the basis of a question answering Alexa skill by demonstrating a feasible way that the resources offered by Amazon can be utilized in order to build such an application.

Contributors

Created

Date Created
2019-05

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Data Analysis in Appointment Scheduling

Description

Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing

Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing schedules. The insights are generated by comparing patients’ preferred appointment times with the current schedule coverage and calculating utilization of past appointments. While untested in the field, the project yielded promising results using generated sample data as a proof of concept for the benefits of using data analytics to remove deficiencies in a health care office’s schedule.

Contributors

Agent

Created

Date Created
2020-05

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On Monitoring and Predicting Mobile Network Traffic Abnormality

Description

Traffic analysis and traffic abnormality detection are emerged as an efficient way of detecting network attacks in recent years. The existing approaches can be improved by introducing a new model and a new analysis method of network user’s traffic behaviors.

Traffic analysis and traffic abnormality detection are emerged as an efficient way of detecting network attacks in recent years. The existing approaches can be improved by introducing a new model and a new analysis method of network user’s traffic behaviors. The description dimensions to network user’s traffic behaviors in the current approaches are high, resulting in high processing complexity, high delay in differentiating an individual user’s abnormal traffic behavior from massive network data, and low detection rate. To improve the detection rate and efficiency, we develop a new method of establishing user’s traffic behavior analysis system based on a new model of network traffic monitoring. First, we establish a more complete feature set based on the characteristics of network traffic to describe massive network user’s behaviors. Then, we define a feature selection rule based on the relative deviation distance to select the optimized feature set. We use the selected feature set to locate the abnormality moment and the users who produce the abnormal traffic behavior. Finally, a traffic behavior analysis method based on prediction is developed to improve efficiency of the system. This new method is applied to evaluate the mobile users on mobile cloud. The experimental results show that the proposed method has a higher detection rate and lower delay in the analysis of abnormal user’s traffic behavior than that of the existing approaches.

Contributors

Agent

Created

Date Created
2015-01-01

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Design and Analysis on Trusted Network Equipment Access Authentication Protocol

Description

Cloud security is a system engineering problem. A common approach to address the problem is to adapt existing Trusted Network Connection (TNC) framework in the cloud environment, which can be used to assess and verify end clients’ system state. However,

Cloud security is a system engineering problem. A common approach to address the problem is to adapt existing Trusted Network Connection (TNC) framework in the cloud environment, which can be used to assess and verify end clients’ system state. However, TNC cannot be applied to network equipment attached to the cloud computing environment directly. To allow the network devices to access the trusted network devices safely and reliably, we first developed a Trusted Network Equipment Access Authentication Protocol (TNEAAP). We use the BAN logic system to prove that TNEAAP is secure and credible. We then configure the protocol in an attack detection mode to experimentally show that the protocol can withstand attacks in the real network. Experiment results show that all the nine goals that decide the protocol’s security have been achieved.

Contributors

Agent

Created

Date Created
2015-02-01

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A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

Description

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods.

Contributors

Agent

Created

Date Created
2016-11-11

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A Security Authentication Protocol for Trusted Domains in an Autonomous Decentralized System

Description

Software Defined Network (SDN) architecture has been widely used in various application domains. Aiming at the authentication and security issues of SDN architecture in autonomous decentralized system (ADS) applications, securing the mutual trust among the autonomous controllers, we combine trusted

Software Defined Network (SDN) architecture has been widely used in various application domains. Aiming at the authentication and security issues of SDN architecture in autonomous decentralized system (ADS) applications, securing the mutual trust among the autonomous controllers, we combine trusted technology and SDN architecture, and we introduce an authentication protocol based on SDN architecture without any trusted third party between trusted domains in autonomous systems. By applying BAN predicate logic and AVISPA security analysis tool of network interaction protocol, we can guarantee protocol security and provide complete safety tests. Our work fills the gap of mutual trust between different trusted domains and provides security foundation for interaction between different trusted domains.

Contributors

Agent

Created

Date Created
2015-12-30

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Tenga: A Case Study on Building Web Applications

Description

Tenga is an e-commerce demo web application for students studying Distributed Software Development and Software Integration and Engineering at Arizona State University (ASU). The application, written in C#, aims to empower students to understand how complex systems are build. Complementing

Tenga is an e-commerce demo web application for students studying Distributed Software Development and Software Integration and Engineering at Arizona State University (ASU). The application, written in C#, aims to empower students to understand how complex systems are build. Complementing the two courses taught at ASU, it seeks to demonstrate how the concepts taught in the two classes can be applied to the real world. In addition to the practical software development process, Tenga also bring in the topics that students are inexperienced with such as recommendation systems and ranking algorithms. Tenga is going to be used in classrooms to help students to learn fundamental issues in Web software development and software integration and to understand tools and skill sets required to built a web application.

Contributors

Created

Date Created
2016-05