Temporal variations of IEEE 802.11b signal strengths in an in-building environment

Recent popularity of the IEEE 802.11b Wireless Local Area Networks (WLANs) in a host of current-day applications has instigated a suite of research challenges. The 802.11b WLANs are highly reliable and wide spread. In this work, we study the temporal characteristics of RSSI in the real-working environment by conducting a controlled set of experiments. Our results indicate that a significant variability in the RSSI can occur over time. Some of this variability in the RSSI may be due to systematic causes while the other component can be expressed as stochastic noise. We present an analysis of both these aspects of RSSI. We treat the moving average of the RSSI as the systematic causes and the noise as the stochastic causes. We give a reasonable estimate for the moving average to compute the noise accurately. We attribute the changes in the environment such as the movement of people and the noise associated with the NIC circuitry and the network access point as causes for this variability. We find that the results of our analysis are of primary importance to active research areas such as location determination of users in a WLAN. The techniques used in some of the RF-based WLAN location determination systems, exploit the characteristics of the RSSI presented in this work to infer the location of a wireless client in a WLAN. Thus our results form the building blocks for other users of the exact characteristics of the RSSI.

Perceptions of New Principal Mentoring Programs in a Large Urban School District

The principalship has changed significantly over the past 20 years. Today’s principals must be effective instructional leaders, managers of large facilities, and experts at analyzing data to successfully meet the accountability demands of high-stakes testing, along with state, and federal mandates. The primary purpose of this quantitative study was to examine how 43 first- and second-year sitting school principals perceived their mentoring experiences and the degree to which a principal mentoring program—offered by their large urban school district—was effective in building their leadership capacity. A second purpose of this inquiry was to understand these principals’ perceptions of the most beneficial aspects of the mentoring program. The study used quantitative data gathered via an online questionnaire distributed during Fall 2015. The results indicated that respondents perceived that the components of the large urban school-mentoring program were generally effective in training principal mentees to become highly-effective school leaders. This study enriches the literature on mentoring by providing the voices of first and second year school leaders to add depth to the characteristics of successful mentoring programs.

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

A Better Place to Be: Republicanism as an Alternative to the Authoritarianism-Democracy Dichotomy

ABSTRACT Title of Dissertation: A BETTER PLACE TO BE: REPUBLICANISM AS AN ALTENATIVE TO THE AUTHORITARIANISM-DEMOCRACY DICHOTOMY Christopher Ronald Binetti, Doctor of Philosophy, and 2016 Dissertation directed by: Dr. Charled Frederick Alford, Department of Government and Politics In this dissertation, I argue that in modern or ancient regimes, the simple dichotomy between democracies and autocracies/dictatorships is both factually wrong and problematic for policy purposes. It is factually wrong because regimes between the two opposite regime types exist and it is problematic because the either/or dichotomy leads to extreme thinking in terms of nation-building in places like Afghanistan. In planning for Afghanistan, the argument is that either we can quickly nation-build it into a liberal democracy or else we must leave it in the hands of a despotic dictator. This is a false choice created by both a faulty categorization of regime types and most importantly, a failure to understand history. History shows us that the republic is a regime type that defies the authoritarian-democracy dichotomy. A republic by my definition is a non-dominating regime, characterized by a (relative) lack of domination by any one interest group or actor, mostly non-violent competition for power among various interest groups/factions, the ability of factions/interest groups/individual actors to continue to legitimately play the political game even after electoral or issue-area defeat and some measure of effectiveness. Thus, a republic is a system of government that has institutions, laws, norms, attitudes, and beliefs that minimize the violation of the rule of law and monopolization of power by one individual or group as much as possible. These norms, laws, attitudes, and beliefs ae essential to the republican system in that they make those institutions that check and balance power work. My four cases are Assyria, Persia, Venice and Florence. Assyria and Persia are ancient regimes, the first was a republic and then became the frightening opposite of a republic, while the latter was a good republic for a long time, but had effectiveness issues towards the end. Venice is a classical example of a medieval or early modern republic, which was very inspirational to Madison and others in building republican America. Florence is the example of a medieval republic that fell to despotism, as immortalized by Machiavelli’s writings. In all of these examples, I test certain alternative hypotheses as well as my own.

"Natural Enemies" or Intentional Allies? Teachers' & Parents' Perspectives on Middle School Boys of Color

This study examined the perspectives and “shared knowledge” of parents and teachers of boys of color. The following overarching research question guided this study: “What do parents and teachers want each other to know about their middle school son or student of color regarding academics, engagement, and behavior?” Additionally, it explored the challenges and opportunities for shared knowledge and understanding of their (respective) son’s’ or students’ academics and engagement. The methodology was qualitative in nature and the intent in conducting this case study was to describe, interpret, and explain the “shared knowledge” between these stakeholders at a predominantly minority middle school. A sample of seven parents and seven teachers from one school in a mid-Atlantic state participated in interviews and focus groups. Results indicated that parents and teachers of boys of color viewed each other as “intentional allies.” Results further showed that parents and teachers were aware of the challenges faced by boys of color in and out of school. That awareness was reflected in strategies that both groups employed to support, prepare, and protect their son/students. Lastly, the study found that teachers received no formal training in building parent-teacher partnerships, but gathered experimental knowledge on how to build those relationships. These findings have implications for teacher education programs, schools, parents, and teachers.

Trace Oblivious Program Execution

The big data era has dramatically transformed our lives; however, security incidents such as data breaches can put sensitive data (e.g. photos, identities, genomes) at risk. To protect users' data privacy, there is a growing interest in building secure cloud computing systems, which keep sensitive data inputs hidden, even from computation providers. Conceptually, secure cloud computing systems leverage cryptographic techniques (e.g., secure multiparty computation) and trusted hardware (e.g. secure processors) to instantiate a “secure” abstract machine consisting of a CPU and encrypted memory, so that an adversary cannot learn information through either the computation within the CPU or the data in the memory. Unfortunately, evidence has shown that side channels (e.g. memory accesses, timing, and termination) in such a “secure” abstract machine may potentially leak highly sensitive information, including cryptographic keys that form the root of trust for the secure systems. This thesis broadly expands the investigation of a research direction called trace oblivious computation, where programming language techniques are employed to prevent side channel information leakage. We demonstrate the feasibility of trace oblivious computation, by formalizing and building several systems, including GhostRider, which is a hardware-software co-design to provide a hardware-based trace oblivious computing solution, SCVM, which is an automatic RAM-model secure computation system, and ObliVM, which is a programming framework to facilitate programmers to develop applications. All of these systems enjoy formal security guarantees while demonstrating a better performance than prior systems, by one to several orders of magnitude.