I present a time synchronization algorithm for wireless sensor networks that aims to conserve sensor battery power. The proposed method creates a hierarchical tree by flooding the sensor network from a designated source point. It then uses a hybrid algorithm derived from the timing-sync protocol for sensor networks (TSPN) and the reference broadcast synchronization method (RBS) to periodically synchronize sensor clocks by minimizing energy consumption. In multi-hop ad-hoc networks, a depleted sensor will drop information from all other sensors that route data through it, decreasing the physical area being monitored by the network. The proposed method uses several techniques and thresholds to maintain network connectivity. A new root sensor is chosen when the current one's battery power decreases to a designated value. I implement this new synchronization technique using Matlab and show that it can provide significant power savings over both TPSN and RBS.

In this dissertation, I start with an analysis of negation in eleven benchmark corpora covering six Natural Language Understanding (NLU) tasks. With a thorough investigation, I first show that (a) these benchmarks contain fewer negations compared to general-purpose English and (b) the few negations they contain are often unimportant. Further, my empirical studies demonstrate that state-of-the-art transformers trained using these corpora obtain substantially worse results with the instances that contain negation, especially if the negations are important. Second, I investigate whether translating negation is also an issue for modern machine translation (MT) systems. My studies find that indeed the presence of negation can significantly impact translation quality, in some cases resulting in reductions of over 60%. In light of these findings, I investigate strategies to better understand the semantics of negation. I start with identifying the focus of negation. I develop a neural model that takes into account the scope of negation, context from neighboring sentences, or both. My best proposed system obtains an accuracy improvement of 7.4% over prior work. Further, I analyze the main error categories of the systems through a detailed error analysis. Next, I explore more practical ways to understand the semantics of negation. I consider …

There is an increased use of block-based programming environments in K-12 education and computing outreach activities to introduce novices to programming and computational thinking skills. However, despite their appealing design that allows students to focus on concepts rather than syntax, block-based programming by design is inaccessible to people with visual impairments and people who cannot use the mouse. In addition to this inaccessibility, little is known about the instructional experiences of students with visual impairments on current block-based programming environments. This dissertation addresses this gap by (1) investigating the challenges that students with visual impairments face on current block-based programming environments and (2) exploring ways in which we can use the keyboard and the screen reader to create block-based code. Through formal survey and interview studies with teachers of students with visual impairments and students with visual impairments, we identify several challenges faced by students with visual impairments on block-based programming environments. Using the knowledge of these challenges and building on prior work, we explore how to leverage the keyboard and the screen reader to improve the accessibility of block-based programming environments through a prototype of an accessible block-based programming library. In this dissertation, our empirical evaluations demonstrate that people …