Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.

Edge computing and CAV (Connected Autonomous Vehicle) fields can work as a team. With the short latency and high responsiveness of edge computing, it is a better fit than cloud computing in the CAV field. Moreover, containerized applications are getting rid of the annoying procedures for setting the required environment. So that deployment of applications on new machines is much more user-friendly than before. Therefore, this paper proposes a framework developed for the CAV edge computing scenario. This framework consists of various programs written in different languages. The framework uses Docker technology to containerize these applications so that the deployment could be simple and easy. This framework consists of two parts. One is for the vehicle on-board unit, which exposes data to the closest edge device and receives the output generated by the edge device. Another is for the edge device, which is responsible for collecting and processing big load of data and broadcasting output to vehicles. So the vehicle does not need to perform the heavyweight tasks that could drain up the limited power.

Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.

Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.

With the rapid development of mobile device technology, smartphones are now not only the tool for young people but also for elderly people. However, the complicated steps of interacting with smartphones are stopping them from having a good user experience. One of the reasons is that application designers do not take consideration of the user group of elderly people. Our pilot survey shows that most elderly people lack the skills required to use a smartphone without obstacles, like typing. We also conducted an experiment with 8 participants that targeting on the usability of a daily used application, Contact List (CL), and based on a Chinese language system. We developed an android application that proposed a new method of showing the contact list according to the language usage of Chinese for this study. By asking participants to finish the same tasks on the traditional CL applications on their phones or on our application and observing their operations, we obtained useful feedback in terms of usability issues. Our experiment also tried to find out whether the method we proposed in the new application can lead to a better user experience for elderly people.

On-boarding refers to bringing in an employee to a company and granting access to new hires. However, a person may go through different stages of employment, hold different jobs by the same employer and have different levels of information access during the employment duration. A shared services organization may have either limited or wide-spread access within certain groups. Off-boarding implies the removal of access of information or physical devices such as keys, computers or mobile devices when the employee leaves. Off-boarding is the management of the separation an employee from an institution. Many organizations use different steps that constitute the off-boarding process. Incomplete tracking of an employee's access is a security risk and can lead to unintended exposure of company information and assets. Blockchain technology combines blocks of information together using a cryptographic algorithm based on the existing previous block and is verified by the peers in the blockchain network. This process creates an immutable record of employee system access providing an audit trail of access for any point in time to ensure that all access permissions can be removed once employment ends. This project proposes using blockchain technology to consolidate information across disparate groups, and to automate access removal …

An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two categories. First, we distinguish whether something edible resulted from the event. Second, if something edible resulted, we distinguish between perfect, partial and alternative outcomes. The main contributions of this thesis are a corpus of 4,000 tweets annotated with event outcome information and experimental results showing that the task can be automated. The corpus includes tweets that have only text as well as tweets that have text and an image.

The purpose of this study is to propose a top-down policy engineering framework for attribute-based access control (ABAC) that aims to automatically extract ACPs from requirement specifications documents, and then, using the extracted policies, build or update an ABAC model. We specify a procedure that consists of three main components: 1) ACP sentence identification, 2) policy element extraction, and 3) ABAC model creation and update. ACP sentence identification processes unrestricted natural language documents and identify the sentences that carry ACP content. We propose and compare three different methodologies from different disciplines, namely deep recurrent neural networks (RNN-based), biological immune system (BIS-based), and a combination of multiple natural language processing techniques (PMI-based) in order to identify the proper methodology for extracting ACP sentences from irrelevant text. Our evaluation results improve the state-of-the-art by a margin of 5% F1-Measure. To aid future research, we also introduce a new dataset that includes 5000 sentences from real-world policy documents. ABAC policy extraction extracts ACP elements such as subject, object, and action from the identified ACPs. We use semantic roles and correctly identify ACP elements with an average F1 score of 75%, which bests the previous work by 15%. Furthermore, as SRL tools are often …

Epilepsy affects up to 1% of the world's population and approximately 2.5 million people in the United States. A considerable portion (30%) of epilepsy patients are refractory to antiepileptic drugs (AEDs), and surgery can not be an effective candidate if the focus of the seizure is on the eloquent cortex. To overcome the problems with existing solutions, a notable portion of biomedical research is focused on developing an implantable or wearable system for automated seizure detection and control. Seizure detection algorithms based on signal rejection algorithms (SRA), deep neural networks (DNN), and neighborhood component analysis (NCA) have been proposed in the IoMT framework. The algorithms proposed in this work have been validated with both scalp and intracranial electroencephalography (EEG, icEEG), and demonstrate high classification accuracy, sensitivity, and specificity. The occurrence of seizure can be controlled by direct drug injection into the epileptogenic zone, which enhances the efficacy of the AEDs. Piezoelectric and electromagnetic micropumps have been explored for the use of a drug delivery unit, as they provide accurate drug flow and reduce power consumption. The reduction in power consumption as a result of minimal circuitry employed by the drug delivery system is making it suitable for practical biomedical applications. …

Cloud-based collaborative editors enable real-time document processing via remote connections. Their common application is to allow Internet users to collaboratively work on their documents stored in the cloud, even if these users are physically a world apart. However, this convenience comes at a cost in terms of user privacy. Hence, the growth of popularity of cloud computing application stipulates the growth in importance of cloud security. A major concern with the cloud is who has access to user data. In order to address this issue, various third-party services offer encryption mechanisms for protection of the user data in the case of insider attacks or data leakage. However, these services often only encrypt data-at-rest, leaving the data which is being processed potentially vulnerable. The purpose of this study is to propose a prototype software system that encrypts collaboratively edited data in real-time, preserving the user experience similar to that of, e.g., Google Docs.

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, …