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- Creator:
- Rajguru, Aniruddha Shekhar
- Description:
- Over the years, data privacy has been a major concern amongst consumers. Applications such as Facebook, Uber, and Instagram collect a huge amount of data from users in return for the free service. Some of this data collection is necessary for the service to work. However, the data being collected is often not essential for the functionality but is rather used for targeted advertising or user analytics. As the data collection takes place in the background, most consumers are left clueless. Consumers also lack the technical expertise to identify such data collection. Not just third-party applications, but even the Android operating system itself sometimes violates users’ privacy heavily. There are various ways of collecting user data, one of which is using device sensors, such as microphones, cameras, GPS, Wi-Fi and accelerometer, to precisely monitor the users’ activity. The goal of this project is to create a sensor monitor that allows users to view and capture accurately what happens to their data on a day-to-day basis. The sensor monitor also informs users to view which applications are accessing which sensors and at what time. To achieve this functionality, the sensor monitor targets three parts of the Android stack: the Linux kernel’s PROC file system, Android’s SensorManager utility, and sensor.h header file. Combining these metrics along with a flag status allows the sensor monitor to form historical insights and send real-time alerts. The sensor monitor is designed to be modular for better maintainability and extensibility. All sensor monitor insights are stored in JSON and can be easily exported for further analysis. Thus, the sensor monitor will benefit a regular smartphone user as well as form a base for future projects in the Android domain.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Luman, Rijul
- Description:
- Since the rise of internet and e-commerce, buying and selling goods over the internet has relied heavily on financial institutions acting as 3rd parties to process financial transactions. These 3rd parties often charge a good percentage of the payment as transaction fees and often take days to complete. These processes are therefore based upon the 2 parties trusting a 3rd party to process their transaction, and as a 3rd party must be “trusted” there is always room for a transaction to be reversed. Before Bitcoin, there was no way to make a non-reversible payment online for a non-reversible service as there is with cash in the physical world. With the recent price rise of Bitcoin, we have witnessed the impact a decentralized digital currency can have on the world. Mining Bitcoins require special hardware; thus, I have developed a coin using JavaScript, which can be mined on any device with minimal overhead. All services are accessible via REST APIs to the Full Node. This project contains the following features: 1. Ability to create a new Public/Private Key pairs (Wallet address). 2. A blockchain which maintains all the transactions and acts as a Ledger. 3. Ability to update the blockchain and manage forks in the blockchain. 4. Send and receive coins. Each coin can be split up to 6 decimal places. 5. Ability to check any wallet’s balance, using its public key. 6. Ability to connect to other full nodes via the internet. 7. Miner Software, which will process all the unconfirmed transactions.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Kollu, Sindhu
- Description:
- In modern economic times, everyone wants to save their money and keep expenses with in their budget limit. But, due to our busy schedules in our day to day life, we may lose track of our expenses and end up overspending which leads to debts. In this project, the aim is to develop a mobile application which helps user to keep track of all the expenses and to simplify the tracking process. The proposed system will also generate detailed information on which category of shopping we are exceeding our budgets so that the user can monitor and keep his expenses with in his budget limit. The rapid growth in technology has led to smart mobile applications development. The proposed application utilizes Optical Character Recognition(OCR) engine to scan the shopping receipts and extract the data such as merchant name, merchant type, date of purchase and the amount spent on each shopping item on any specific day. Optical character recognition is a technique to convert any printed text or document into digital text. The data extracted after performing OCR is then visualized using various insightful charts and reports which will help the users to estimate and keep track of their expenses to be within the budget limit. Smart budget assistant is an Android mobile application which simplifies the process of entering expenses and tracking them to help users keep their expenses with in their budgets by informing about their limits and visualizing detailed reports for quick analysis to save time and money.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Jain, Anshul
- Description:
- Indoor localization has become one of the most talked about services in today’s technology. We have observed that there have been huge demand of Indoor Location services due to increase in smartphone market in last few years. GPS is widely used to find the real time location information of different mobile users mainly in outdoors. This is the main reason there is a large demand for real time location prediction of various mobile users. However, GPS is not effective in indoor buildings due to its weak signal strength and has been found in the research that GPS does not work properly in indoor environment. Wi-Fi access points are widely used in indoor localization techniques which are based on Wi-Fi fingerprint data. Many research papers have been proposed on Wi-Fi fingerprinting based methods using its signal strength generated from Wi-Fi access points. They have proposed that Wi-Fi fingerprinting can increase indoor localization accuracy using different methods like collected Wi-Fi signal strength in various locations. Indoor localization using machine learning techniques is still an open area in which so much research is still going on to find out the best indoor localization. Our goal in this project is to do the comparative analysis of indoor localization using the different machine learning models. We will collect Wi-Fi fingerprinting data which has different Wi-Fi signal strength, and access points on different locations using android application in an android smartphone Motorola G4. After then, we will implement and evaluate the indoor positioning accuracy, time complexity and test error on different machine learning/ neural network models. We will present the comparative analysis of best model out of them to use for indoor localization technique in various closed environment. This project will be implemented on the third floor of California state university- Sacramento, Department of computer science after collecting the Wi-Fi fingerprinting data and will perform different test results. We have also found there has not been much research and comparative analysis happened in indoor localization field using the different machine learning algorithms in different scenarios like time duration, accuracy, error and overall performance. Implementation will be done in both the environments CPU and GPU using python programming language and different related libraries.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Devarakonda, Divya Sindhuri
- Description:
- USA has been grappling with crime for decades now and had made significant improvement. However, crime remains to be one of the core societal problems. To build a safer society, we need to take advantage of 21st century’s technology. With current technologies and data availability it is possible to analyze crime patterns and forecast future occurrences of crime. This information is useful for police to increase safety measures and alert the local residents. ‘Predictive policing’ is one such aspect under implementation in few states by the government of USA. This project analyzes and compares the patterns of ‘Chicago’ and ‘Los Angeles’ crime based on history and forecasts future crime rate. These results potentially could help immigrants to choose their area of residence and can help tourists, students and travelers to plan their trips in safer months. In this project, ARIMA, Auto ARIMA, Holts winter and Facebook prophet forecasting models are experimented on Chicago and Los Angeles crime Data. Experimental results show that Holt’s winter and Facebook prophet models give accurate forecasting with Mean Absolute Percentage Error(MAPE) of 9 on one year ahead forecasts.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Poon, Dennis Yiuwing
- Description:
- Refactoring is a process used by programmers to improve the non-functional qualities of code. It does not affect the actual output of the program, but it is useful for making the code easier to read and maintain. In practice, it often requires a lot of programming experience to implement refactoring properly, making it challenging for novice programmers. There are existing tools that can help facilitate the refactoring process. However, none of them can provide meaningful guidance to a programmer with regards to what refactoring methods should be used, or how to. This project proposes an Eclipse plug-in tool that provides a refactoring knowledge base for developers to reference. It allows developers to understand when to use and how to implement a refactoring method. In addition, the knowledge base presents related refactoring methods in order to create refactoring sequences. This tool can also help keep track of common refactoring patterns for a programmer or a target code base.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Shah, Jinaliben
- Description:
- Active database systems support applications by moving the reactive behavior from applications into database management systems. It is a database system that includes an event-driven architecture that can respond to different types of events automatically. Graph databases use graph structures such as nodes and edges to store data. Cypher is a declarative query language and it is used to store and retrieve data from the Neo4j database system. This project incorporated active rules into graph databases, focusing on business rules instead of integrity constraints. The project consists of two parts: a language framework and an execution model. There are two types of rules that are triggered by Temporal Events and Mutation Events, respectively. The language framework had been completed by another master student. This project focused on the execution part and refining the language model. The execution system consists of two interfaces, i.e. Active Rule Specification Interface and Query Interface. The Active Rule Specification Interface is used to specify rules and the Query Interface allows user to input cypher commands such as insert, update and delete. The flow of the execution model is as follows. Users can specify active rules through the rule Creation Interface. The rule parser parses the rules and stores the rules in the Active Rule Repository. The event handler checks whether an event can be raised or not upon cypher commands. The event handler will notify the rule engine once an event occurs. Rule engine retrieves rules which are triggered by the event from the active rule repository. The main contribution of this project is the architecture design as well as the design and implementation of the execution model to incorporate active rules into graph databases.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Palanisamy, Sharmila
- Description:
- A stochastic prediction modeling method such as Hidden Markov Model (HMM) is a complex system. The HMM is suitable to analyze and predict time-dependent phenomena and has been widely used in applications such as speech recognition, weather and stock market forecasts. In California, forest wild fires are a form of natural calamity. The causes of wildfires have many factors and studies for a prediction method are much needed. We believe that the occurrences of wild fires can be a time-dependent phenomenon and HMM is a good candidate for predicting wildfire probabilities. California wildfire and weather datasets are used in HMM to generate wildfire prediction outcomes and then compared to historic records. This project focuses on two approaches. First approach is to train two HMMs. One model that can identify patterns that cause high number of wildfires. Another model that identifies patterns that cause low number of wildfires. Using the trained HMM parameters and the observations in test data, likelihood value is computed for each category of HMM. Prediction is done by comparing the likelihood values and then compared with the actual historical data. Second approach is to predict trends in future wildfires. In this approach, an HMM is trained using "n" years of data. This training creates hidden state sequences inside the model. For predicting "n+1th" year, likelihood values are computed over a range of possible observation values. The one with the maximum likelihood value, is predicted to be the most likely "next" observation.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Shenoy, Shweta
- Description:
- Security and usability have been on the opposite ends of the spectrum; sometimes, to achieve one, the other must be compromised to some extent. Passwords are a typical example in which usability, psychology, and security meet. Absurd password rules force users to create complex passwords for the sake of enhanced security. However, users often struggle to create and recall such passwords and resort to techniques such as writing them down, reusing them, and storing them in vulnerable ways. The use and management of passwords have become one of the biggest challenges for users and security experts today. The strength of a password directly correlates to its security. In addition, we define the pronunciability of a password as a means to measure how easy it is to memorize – an aspect we associate with usability. These metrics, along with the opinions of real users from an online survey, will be used to empirically analyze the relationship between usability and security in user passwords. This project analyzes a dataset of 300,000 passwords, to determine whether the user-generated passwords are both usable and secure. By quantifying the password strength and predicting the pronunciability of a password, we design a framework to map the relationship between the two. We find that passwords are either secure or usable, but they rarely ace in both aspects. Furthermore, we suggest how password creation strategies can be adapted to better align with usable security.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Pinto, Rodney
- Description:
- One of the most critical resources that ensure the current working of the internet is the domain name system (DNS). It is a decentralized, hierarchical naming system that is responsible for translating the human-readable domain name to its associated IP address. The use of DNS thus eliminates the need for humans to remember the IP address of all their favorite websites (such as 172.217.6.68 an IPV4 address for google.com). Despite its widespread use, DNS is vulnerable to various security issues. This project focuses on replicating the basic functionality of the existing DNS on the blockchain and deploying it on a peer to peer network making it completely decentralized and, in the process, make it a bit more secure and reliable by addressing few of the security vulnerabilities of the existing system
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science