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- Creator:
- Rawat, Anand
- Description:
- Social networks are the hub of social interactions in today’s world and conversations are a treasure trove of sentiments on these platforms. Currently, to the best of our knowledge, there is no system in place in any of the social networking platforms to predict the favorability of a ‘post’ through sentiment analysis of existing discussion on topics throughout the social networks. Having this system will allow the user to carefully reorganize their opinion so that the interpretation is well aligned with the users’ opinion and their audiences’ sentiment. However, such analysis cannot be performed effectively with the use of traditional statistical methods due to the inherent complexity of the problem. In this project, we aim to design and implement a system to support the users in predicting the favorability of a ‘post’. The proposed system consists of two modules. The first one is a sentiment analysis deep learning network using Bidirectional LSTM layers, which predicts the sentiment score of each reply present in the conversation tree collected as a part of the dataset using twitter API. This model is trained using Sentiment140 dataset [1]. The second module leverages the classification results from the first module to perform regression analysis on original tweets. This model is trained on the dataset collected using twitter API. Specifically, the output of the second module is the favorability score of any given tweet, which is based on the predicted percentage of positive responses. Our project also reveals the significant performance gain of using word embedding techniques, e.g., GloVe [2], over the traditional count or TF-IDF based word vector representation. The data for this project has two origins. While the labeled data for sentiment analysis was taken from Sentiment140 website [1], the data for twitter conversation tree was collected using twitter API.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Colson, Michael Bradley
- Description:
- One of the main responsibilities of an Operating System (OS) is to ensure system stability and security. Most OSes utilize mechanisms built into the CPU hardware to prevent user-mode processes from altering the state of the system or accessing protected spaces and hardware I/O ports. A process should only read from or write to memory addresses in its address space. In order to accomplish the above, the CPU is typically designed to operate in two or more modes with different privilege levels. The OS typically runs in the mode with highest privilege level, which enables it to execute any instruction and gives it access to all the memory in the system. To improve system reliability, security and stability, applications usually run in the mode with the lowest privilege level. At Sacramento State University, CSC 159 (Operating System Pragmatics) students learn to develop an OS using the SPEDE (System Programmer’s Educational Development Environment) framework. SPEDE provides an environment to build an OS executable image and download it to a target system where it is executed as the local OS. While SPEDE provides an excellent development environment for students to learn how to develop an OS, one limitation is it does not currently support running processes in user-mode (only kernel-mode is supported). In order to give students a better understanding of how an OS can provide security and protection, it is necessary to enable the support for user-mode processes. The goals of this project are to establish a supportive software component for the creation of user-mode processes, enabling and enforcing memory protection for the process runtime and handling of runtime exceptions in a virtual memory paging system, such as page faults and general protection faults in the OS kernel. In order to achieve these goals, additional kernel data structures must be implemented (e.g. paging table structures, a Task State Segment, modified process trap frame, etc.) along with handlers for page faults and general protection faults.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Ahmed, Satia
- Description:
- MongoDB is a document based NoSQL database system which stores data in the form of JSON documents. The de-normalized structure of MongoDB document makes query performance efficient and cost effective. In this project, a daycare application is implemented using MongoDB as the database. To emulate real life data, Big Data documents are generated programmatically using data generator tool from Redgate Software Ltd, called SQL Data Generator, Visual Studio SSIS tool, and python scripting. To connect with the database and perform CRUD (Create, Read, Update, Delete) operations on the data using RESTful API, multiple methods were surveyed in this project. These studies include dependencies and methods to wrap MongoDB with Java, C# and nodeJS. Further, this project implemented RESTful API using nodeJS, wrapping MongoDB as the backend database. The frontend was rendered with AngularJS which communicates with the backend and allows users to query and operate on the data stored in MongoDB. A MVC (Model View Controller) vi model was used to implement the REST API, where Model is our MongoDB database and AngularJS is our View.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Kamble, Nikhita
- Description:
- Bug tracking is defined as a procedure of tracking reported software bugs of any product of an organization. While developing any product or software, there are chances of bug occurrences. Keeping track of these bugs/issues manually is not feasible. At the same time, it is also important to keep a detailed record of the bug fixing information, such as the person who fixed the bug, the time of bug fixing, etc. Such information will help developers to resolve similar issues if they occur in future. Therefore, developing a tool for bug tracking is essential. Using a web based bug tracking system would not only satisfy the company needs of bug tracking, but would also facilitate the discussion and problem solving among teams at different locations. Tools such as JIRA, Redmine, and so on are used by many organizations for bug tracking. However, issues such as high cost, unrequired features, non-user friendly interface might create hassle for small start-up companies. The free tools available usually contain advertisements and require installation of unnecessary software.This results in waste of memory space and creates potential security problems. Attackers could possibly leverage such free tools to conduct attacks towards the host machine. The purpose of this project is to develop a tool which is user-friendly, inexpensive, advertisement free, and secure for start-up organizations. The proposed bug tracking system is user friendly, easy to install, and light-weight. It has low system requirement. Cryptography technique has been used for encrypting some fields of issues to protect the information credentiality. In addition, customized version can be developed given specific features required by organizations.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Vemuri, Pujitha
- Description:
- With advances in technology, there is a constant need of sharing resources in the form of social media, blogs, and through links to websites. To make sharing easier, URL shortening services like bit.ly, goog.gl, tinyurl.com, ow.ly are used, but often lead to unforeseen issues. The seemingly benign short URLs conceal malicious content. For example, a user who visits malicious website can become a victim of malicious activities such as phishing, spamming, social engineering, and drive-by-download. This project aims to detect malicious shortened URLs with the help of machine learning techniques. Random Forest is one of the best classification algorithms that has a higher accuracy rate as it employs the use of higher number of trees, splitting points and the bagging concept. The model is trained with the shortened URL dataset, along with its features, thus achieving the accuracy of 96.29% for this project. An extension for Chrome is developed to detect the shortened malicious URLs with the use of our generated machine learning model. While using the extension, if it encounters a malicious shortened URL, it informs user with details like normal form of the URL, risk percentage (which depends on accuracy) and with the option of ‘still load the webpage’ or ‘go back’. This extension acts as a barrier between users and malicious websites, helping them educate choices to ensure their safety and privacy.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Saxena, Prachi
- Description:
- With the high increase of Web applications and websites devolvement and usage, monitoring web applications and websites became a necessity to minimize the risk of vulnerability, failures and customer dissatisfaction. The aim of this project is address these issues and develop a centralized service monitoring tool to conduct assessment and collecting relevant Information of the targeted system. This tool is designed to provide more responsive access and cost-effective notification to system issues. Furthermore, the tool would allow the creation of alerts by generating a report for errors and issues, Monitors overall system status at a single source and helps reduce service downtime and improve the overall user experience. The tool services will be provided without SMTP obligations and be using Power Shell and AWS (SNS) Web Monitoring Service.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Bhatia, Bhuvan
- Description:
- Nowadays, the aviation industry plays a crucial role in the world's transportation sector, and a lot of businesses rely on various airlines to connect them with other parts of the world. But, extreme weather conditions may directly affect the airline services by means of flight delays. To solve this issue, accurately predicting these flight delays allows passengers to be well prepared for the deterrent caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance to diminish the negative impact. The purpose of this project is to look at the approaches used to build models for predicting flight delays that occur due to bad weather conditions. In the first part of the project, we look at using Python based Logistic Regression along with Support Vector Machine and then plugging the dataset into our classifier for results. In the second part of the project, we primarily focus on gathering a dataset from Twitter, breaking the dataset down and identifying relevant attributes. Upon examining the results, we compare the results with other models such as Random Forest Classifier and derive the best classifier to solve the problem.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Melnik, Victor Vladimirovich
- Description:
- Following secure coding rules while developing software is challenging, but necessary due to the prevalence of large data breaches attributed to insecure code that have occurred for companies and government entities such as Equifax, Uber, and U.S. Securities and Exchange Commission. Many static analysis tools are available that can find and remediate vulnerable code, however many of them are commercial tools, or if they are open source, do not integrate well with development environments and do not provide feedback. One tool that is both open source and integrates well with the Eclipse Development environment is the Secure Coding Assistant that was developed by Ben White and later enhance by Chen Li at California State University Sacramento (CSUS). Secure Coding Assistant provides support for secure coding rules for the Java programming language that were developed at the CERT division of the Software Engineering Institute at Carnegie Mellon University. Secure Coding Assistant also provides error correction and contracting programming for the Java language. To further enhance the Secure Coding Assistant tool, we provide support for the C programming language by semi-automating a subset of the CERT secure coding rules vi for C. The tool detects CERT rule violations for the Java and C programming languages in the Eclipse Development Environment and provides feedback to aid and educate software developers in secure coding practices as they develop software. The Secure Coding Assistant tool is maintained on GitHub at http://benw408701.github.io/SecureCodingAssistant/.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Kilaru, Sindhura
- Description:
- The 0-1 Knapsack Problem is a combinatorial optimization problem that can be described as follows. Given a set of items with weights and values and a knapsack with a certain capacity, find a subset of the items that fit in the knapsack and produce a maximum profit (sum of values). The 0-1 Knapsack Problem has many applications including the bin packing and the rod cutting problems. In this project, an enhanced branch-and-bound algorithm is developed for the 0-1 Knapsack problem, based on the history utilization technique proposed by Shobaki and Jamal for the Sequential Ordering Problem. The history utilization algorithm stores information about previously generated sub-problems in a history table and uses that information to accelerate the processing of new sub-problems. Experimental results are reported for various synthesized instances ranging in size from 50 to 1000. The history-based breadth-first search algorithm performed 98% better than a breadth-first search strategy without the history information. It successfully solved many instances that timed out with the breadth-first search algorithm without history. The project also involved implementing a parallel (multi-threaded) version of the algorithm that was run on a multi-core system. The experimental results show that running the parallel version of the algorithm on a quad-core machine gives a speedup of up to 35% compared to the single threaded version.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science
- Creator:
- Patil, Toshit
- Description:
- Graphical Processing Units (GPUs) used in embedded systems such as cars, robotics, mobile devices are usually required to run multiple tasks at the same time. But traditionally GPUs are designed to run only one task at a time. Prior studies have introduced schedulers which allow running multiple tasks simultaneously. However, these schedulers were mostly designed for dedicated GPUs. Since embedded systems use System-On-Chip (SoC) GPUs which have different architectures than dedicated GPUs (i.e., the GPU and CPU share the same memory, and all are on the same chip), previously developed schedulers are not directly applicable to embedded systems. Previous studies have been using “concurrent kernel” and “dynamic parallelism” features of the modern GPUs to build schedulers to run multiple tasks simultaneously. This project aims to improve the use of these scheduling features on SoC architecture by utilizing “zero copy” and “unified memory” techniques which eliminate the explicit copies from the CPU to GPU memory and vice-versa. The results show that unified memory is a more effective technique than zero copy for improving the scheduling features of the SoC architecture. It is possible that the findings of this study in the future will allow building schedulers that are customized for SoC GPUs. In this project, the GPU computing language specific for NVIDIA GPUs called CUDA and an emerging SoC GPUs from NVIDIA called Tegra X1 and Tegra K1 are utilized.
- Resource Type:
- Project
- Campus Tesim:
- Sacramento
- Department:
- Computer Science