We have introduced Peer-Educators through the Supplemental Instruction program and four Team-Based Learning laboratory assignments in our Introduction to Programming course. These four laboratory assignments are using Finch Robots to facilitate student comprehension of basic object-oriented programming concepts. In contrast to other lab assignments, Finch labs are team-based activities that revolve around programming Finch, a programmable robot to do activities like moving, sensing the environment, playing music, etc. Introducing the Finch labs has multiple benefits for the students. It helps better anchor the basic concepts of object-oriented programming, since interacting with the Finch robot is done by instantiating a Finch object and performing method calls on the object. This helps students understand many of the object oriented topics of the course, like object instantiation, method calls, passing arguments to a method and changing the state of an object. Furthermore, the labs facilitate peer learning in a natural way, since students group to work on a physical object. The goal of our intervention is to increase retention in our introduction to programming offerings. We are currently on the third year of our project and after having concluded in the interventions we want to adopt we are evaluating them through post-activity surveys, end of the semester surveys and comparison of retention data.

We present a randomized algorithm for asynchronous task allocation, also known as the write-all or do-all problem. Our algorithm has work complexity O(n+k2log3k) with high probability, where n the number of tasks and k the number of processes that participate in the computation. Our solution uses O(n) shared memory space that supports atomic test-and-set operations and with high probability each participating process uses O(k) internal memory space. This is the first adaptive solution for the write-all problem that has work n plus some additive term which depends only on the number of participating processes k and not the size of the problem n.

This paper investigates a location-based authentication system where authentication questions are generated based on users' locations tracked by smartphones. More specifically, the system builds a location profile for a user based on periodically logged Wi-Fi access point beacons over time, and leverages this location profile to generate authentication questions. To evaluate the various aspects of this location-based authentication approach, we deployed the application on users' smartphones and conducted a real-life study for one month with 14 users. To simulate various kinds of adversaries (e.g., Naive vs. Knowledgeable), in our study, we recruited volunteers in pairs (e.g., Friends), in addition to single participants. Over the course of the experiment, each user is periodically presented with two sets of authentication questions. The first set is generated based on a user's own data. The second set is generated based on a randomly selected user's data. Additionally, in cases of paired participants, each user is presented with a third set of questions which is generated based on the user's friend's data. In each case, three different kinds of questions of varying difficulty levels are generated and presented to the user. Finally, we present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns. We also discuss various aspects of location-based authentication mechanisms based on our findings in this paper.

Integration of NFC radios into smartphones is expediting the adoption of mobile devices as the preferred method for accessing physical locations, bank accounts, and other valuable resources. The pervasive nature of authentication using these mobile devices, however, comes with increased security considerations stemming from the possibility of physical loss of the device. To minimize the risk caused by stolen devices, this paper introduces a method for confirming the identity of a device's user based on her recent macroscopic behavior over space and time. The user's behavior is continuously recorded by a set of devices embedded in the environment (e.g., Wi-Fi Access Point) and used to train a probabilistic n-gram model. Subsequently, deviations caused by stolen devices can be detected by comparing the user's recent behavior against the trained model. Our first evaluation results demonstrate the ability of the proposed approach to detect anomalies in the user's behavior without generating a significant number of false alarms.

Integration of Near Field Communication (NFC) sensors into mobile devices has enabled their use for authentication. The ubiquitous nature of authentication using mobile devices comes though with increased security considerations. In addition to the risk of being stolen, mobile devices are increasingly susceptible to different types of software attacks. User-provided passwords such as Personal Identification Numbers (PINs) are often employed to ameliorate these limitations. The use of passwords though, has its own vulnerabilities that are mainly caused by the passwords' static nature and low entropy. To eliminate the security threats caused by untrusted devices and static passwords, we propose the development of new types of biometric authentication, based on macroscopic human behavior.

We present and analyze a wait-free deterministic algorithm for solving the at-most-once problem: how m fail-prone processes perform asynchronously n tasks at most once using shared memory. Our algorithmic strategy provides for the first time nearly optimal effectiveness, which is a measure that expresses the total number of tasks completed in the worst case. Our algorithm's effectiveness equals n-2m+2. This is up to an additive factor of m close to the known effectiveness lower bound n-m+1 and improves on the previously best known deterministic solutions that have effectiveness only n-log m o(n). We also present a work and space complexity analysis for suitable ranges of the algorithm parameters and demonstrate further that (i) we can achieve work O(nm log n log m) and simultaneously effectiveness of n-3m2-m+2, which is asymptotically optimal for any m=o(√n),(ii) we can achieve optimal work up to logarithmic factors Õ(n) and asymptotically optimal effectiveness whenever m=o(3√n).

This paper investigates the feasibility of implementing at-most-once access semantics in a model where a collection of actions is to be performed by failure-prone, asynchronous shared-memory processes. We introduce the At-Most-Once problem for performing a set of n jobs using m processors, and we define the notion of efficiency for such protocols, called effectiveness, that allows the classification of algorithms solving the problem. The effectiveness for an at-most-once implementation is the number of jobs safely completed by the implementation, expressed as a function of the number of jobs n, the number of processes m, and the number of process crashes f. We prove a lower bound of n--f on the effectiveness of any algorithm. We then present two process solutions that offer a trade off between work and space complexity. Finally, we generalize a two-process solution for the multi-process setting using a hierarchical algorithm that achieves effectiveness of n--log m†o(n), coming reasonably close, asymptotically, to the corresponding lower bound.

In recent years, two distinct electronic voting technologies have been introduced and extensively utilized in election procedures: direct recording electronic systems and optical scan (OS) systems. The latter are typically deemed safer, as they inherently provide a voter-verifiable paper trail that enables hand-counted audits and recounts that rely on direct voter input. For this reason, OS machines have been widely deployed in the United States. Despite the growing popularity of these machines, they are known to suffer from various security vulnerabilities that, if left unchecked, can compromise the integrity of elections in which the machines are used. This article studies general auditing procedures designed to enhance the integrity of elections conducted with optical scan equipment and, additionally, describes the specific auditing procedures currently in place in the State of Connecticut. We present an abstract view of a typical OS voting technology and its relationship to the general election process. With this in place, we lay down a ldquotemporal-resourcerdquo adversarial model, providing a simple language for describing the disruptive power of a potential adversary. Finally, we identify how audit procedures, injected at various critical stages before, during, and after an election, can frustrate such adversarial interference and so contribute to election integrity. We present the implementation of such auditing procedures for elections in the State of Connecticut utilizing the Premiere (Diebold) AccuVote OS; these audits were conducted by the UConn VoTeR Center, at the University of Connecticut, on request of the Office of the Secretary of the State. We discuss the effectiveness of such procedures in every stage of the process and we present results and observations gathered from the analysis of past election data.