<p> Authentication is the process of validating the identity of an entity, e.g., a person, a machine, etc.; the entity usually provides a proof of identity in order to be authenticated. When the entity &mdash; to be authenticated &mdash; is a human, the authentication process is called end-user authentication. Making an end-user authentication usable entails making it easy for a human to obtain, manage, and input the proof of identity in a secure manner. In machine-to-machine authentication, both ends have comparable memory and computational power to securely carry out the authentication process using cryptographic primitives and protocols. On the contrary, as a human has limited memory and computational power, in end-user authentication, cryptography is of little use. Although password based end-user authentication has many well-known security and usability problems, it is the de facto standard. Almost half a century of research effort has produced a multitude of end-user authentication methods more sophisticated than passwords; yet, none has come close to replacing passwords. </p><p> In this dissertation, taking advantage of the built-in sensing capability of smartphones, we propose an end-user authentication framework for smartphones &mdash; called ePet &mdash; which does not require any active participation from the user most of the times; thus the proposed framework is highly usable. Using data collected from subjects, we validate a part of the authentication framework for the Android platform. For web authentication, in this dissertation, we propose a novel password creation interface, which helps a user remember a newly created password with more confidence &mdash; by allowing her to perform various memory tasks built upon her new password. Declarative and motor memory help the user remember and efficiently input a password. From a within-subjects study we show that declarative memory is sufficient for passwords; motor memory mostly facilitate the input process and thus the memory tasks have been designed to help cement the declarative memory for a newly created password. This dissertation concludes with an evaluation of the increased usability of the proposed interface through a between-subjects study.</p>

<p> Neuromodulation is a neurophysiological process by which a single neuron can regulate the neural activity of a diverse population of neurons. Sensory integration is a neurobiological process by which the brain combines multiple sensory modality inputs (i.e., vision, proprioception, audition, tactile, olfactory, vestibular, interoception, and taste) into usable functional outputs. In biological systems, neuromodulation and sensory integration have been shown to have a strong influence over action selection (decision-making) and action execution (motor output) respectively. The experiments portrayed in Chapters 1-4 provide empirical and theoretical evidence for neuromodulatory influence over selected actions through predictions of expected costs and rewards. The simulation experiments described in Chapters 5-6 illustrate how sensory integration influences action execution across different neural architectures in visually and memory guided sensorimotor transformation tasks. The implications of these results and future endeavors are discussed in Chapter 7, along with a proposed computational model of both action selection and sensory integration to investigate the dynamics of decision-making influenced by the integration of multiple sensory inputs in order to execute an action.</p>

<p> We introduce a novel approach to evolving spike neural network (SNN) based Spatio-temporal (ST) pattern classifiers that can detect occurrences of hidden structures in a ST data. We test this learning paradigm to find characteristic electrical patterns in visually evoked response potentials (VERPs) generated by an alcoholic brain. </p><p> We cast the alcoholic classification task as a multiple feature selection (FS) problem. The FS problems are grouped under 2 classes: the spatial task and the temporal task. The objective of the spatial FS task is to choose a correct subset of electroencephalogram (EEG) leads (the spatial-features) along with the lead-weighs (numeric attributes) using which a composite signal can be created. The temporal FS task involves detecting temporal patterns that occur more frequently in the alcoholic composite signals than in the control signals. To facilitate the evolution of such a classifier, we introduce design rules for SNN based temporal pattern detectors (TPDs) and novel crossover operators for the simultaneous FS task. </p><p> The conventional techniques for characterizing the alcoholic VERPs use the information in the gamma-band (30 to 50 Hz) to develop a set of feature vectors and then train a classifier using these feature vectors. Using the SNN based evolutionary learning paradigm we were able to solve this problem in 1 step; the SNN performed both temporal feature extraction and classification. Unlike the conventional techniques we did not make any specific assumptions regarding the spectral characteristics of the data; we did not implement a gamma-band filter. Also, we located regions on the skull of an alcoholic subject that produced abnormal electrical activity compared to the controls. These regions are consistent with prior findings in the literature. The classification accuracy was measured as the area under the receiver operator characteristic curve (ROC). The area under the ROC curve for the training set varied from 90.32% to 98.83% and for the testing set it varied from 87.17% to 95.9%.</p>

<p> For every participant role filler in an utterance, speakers must choose to leave it bare (e.g., "the interviewer") or to modify it (e.g., "the interviewer on Fresh Air"). Their decision is the end result of a combination of complex factors ranging from the original message to how distracted the speaker is. When we use corpora to create language models, part of our job is understanding the observable properties in and around an event description that allow us to predict these decisions. A considerable body of work on language production and discourse pragmatics concentrates on measuring noun phrase predictability and other forms of shared knowledge that help determine the balance point between over- and under-specification of a participant role filler. Although the importance of predictability as measured by long-term probabilities has long been recognized, I present a novel quantitative analysis of participant role filler predictability, the structure of the mental lexicon, and how the interaction of these two inform a speaker's internal perception of informativity. Standard Gricean assumptions tend to be efficiency oriented. Speakers will be informative enough but not wastefully so. Using these to model corpus distributions predict that noun phrase modification rates are directly proportional to predictability in order to satisfy the speaker's obligation to always be informative. In contrast, standard Firthian models (built around the idea that "you know a word by the company it keeps") assume spreading activation&mdash;and not efficiency&mdash;is the dominant predictor of usage. Sensitivity to activation's effect predicts that noun phrase modification rates are inversely proportional to predictability. Strongly connected participant role fillers could be easily activated for production while weakly connected participant role fillers would either be mentioned less often or themselves trigger strongly connected features (not normally associated with the head verb) to be primed for production. </p><p> To distinguish between these competing assumptions, I analyze participant role filler modification rates in event descriptions with respect to three indicators: the syntactic and semantic optionality of the role filler, the general predictability of the verb's role fillers, and the predictability of individual pairs of verb/participant role fillers. First, I use insights from linguistic theory to classify verbs and their participant roles into classes of syntactic optionality and semantic optionality. Second, I quantify over a large corpus the general predictability of a verb's participant roles and the specific predictability of each pair of verb/participant role filler. Finally, I model the relationship between the three indicators and modification in order to ascertain whether speakers have a stronger tendency to modify the more predictable participant role fillers, as Grice's Maxim of Relevancy predicts, or a tendency to modify the less predictable participant role fillers, as a Firthian activation-based model predicts. </p><p> I present descriptive statistical models to chart the relationship between predictability, syntactic optionality of a participant role, and semantic optionality of a participant role. In general, verb classes with stronger mental lexicon connections to their participant role fillers according to theory also have more predictable participant role fillers in the British National Corpus. Specifically, syntactically optional direct object verbs and semantically obligatory instrument verbs have more predictable participant role fillers than the opposite, comparable verb class. I also present several linear mixed-effect models to determine how predictive of modification the independent variables of syntactic verb class, semantic verb class, and verb/participant role filler predictability are. According to these models, speakers are significantly more likely to modify the less predicted participant role fillers even when taking into account individual verb and verb class differences. I conclude that mental lexicon accessibility modulates noun phrase realization according to a Firthian activation-based model. For each factor, I discuss possible explanations for the correlations between modification, predictability, and optionality and how these correlations make sense within a larger production model.</p>