More research is needed to be able to fully understand the role that social media plays in elections, specifically in local elections. Candidates need to understand how it works and how they can effectively use this new communication medium. By exploring Diffusion of Innovation Theory, Social Information Processing Theory, and the Two-Way Symmetrical Model of communications this study sought to answer one overarching question: how should a candidate employ social media in a local election? This qualitative, single case study explores the 2014 recall and general election in Yorba Linda, California. Councilman Tom Lindsey and candidate Matt Palmer are the primary subjects of study. Observations were made through analysis of documentation, interviews, and participant and direct observation. The researcher was employed as the campaign manager for both Lindsey's and Palmer's campaigns. The findings support the use of social media in local campaigns on a case by case basis. Determining use depends on the demographics of the voters and the abilities of the candidate. The data suggests that social media needs to be part of comprehensive strategy that includes traditional communication tools. Observations from the case study illustrate the need for candidates to engage in two-way communication that is monitored and regulated. This study begins to establish social media as a tool that candidates can use to inexpensively reach voters in a way that showcases the candidate's personality and allows them to connect on a personal level with constituents. Social media will play a role in politics at all levels.

This paper reports a study that examined the pedagogical knowledge (knowledge and beliefs related to the act of teaching) of two more successful and two less successful adult ESL instructors during planning teaching and post teaching reflection. The verbal reports of their teaching were compared to previous studies (Gatbonton, 2000, 2008; Mullock, 2006) that used stimulated recall to categorize adult ESL instructors' pedagogical thoughts during their instruction. The comparison showed that the previous categories were inadequate to cover the data. Additional codes were added in order to codify all the data after which patterns and themes emerged that overarched the previous categories. The five pattern themes among the four participants included academic focus, comprehension, engagement, language management, and student centered. The two more successful teachers each had one specific pattern theme whose fundamental focus was on student learning. These themes dominated the more successful teachers' pedagogical foci while the other four themes were subservient to that dominant theme. Like the more successful teachers all five pattern themes were present in the planning and reflection of the less successful teachers. However, the protocols of the less successful Adult ESL teachers did not exhibit a central theme or pedagogical focus that orchestrated and directed the movement of their pedagogical thoughts among the remaining pattern themes. This lack of a dominant theme meant that the pedagogical foci of these teachers moved from one theme to another without a consistent orientation toward a central goal. The conflicted or divided nature of the pedagogical thinking of these less successful teachers may contribute to the reduction in the learning of students in their classes.

This thesis investigates imbalanced Swedish text financial datasets, specifically receipt classification using machine learning models. The study explores the effectiveness of under-sampling and over-sampling methods for Naive Bayes algorithms, collaborating with Fortnox for a controlled experiment. Evaluation metrics compare balancing methods regarding the accuracy, Matthews's correlation coefficient (MCC) , F1 score, precision, and recall. Findings contribute to Swedish text classification, providing insights into balancing methods. The thesis report examines balancing methods and parameter tuning on machine learning models for imbalanced datasets. Multinomial Naive Bayes (MultiNB) algorithms in Natural language processing (NLP) are studied, with potential application in image classification for assessing industrial thin component deformation. Experiments show balancing methods significantly affect MCC and recall, with a recall-MCC-accuracy tradeoff. Smaller alpha values generally improve accuracy.  Synthetic Minority Oversampling Technique  (SMOTE) and Tomek's algorithm for removing links developed in 1976 by Ivan Tomek. First Tomek, then SMOTE (TomekSMOTE)  yield promising accuracy improvements. Due to time constraints, Over-sampling using SMOTE and cleaning using Tomek links. First SMOTE, then Tomek (SMOTETomek) training is incomplete. This thesis report finds the best MCC is achieved when $\alpha$ is 0.01 on imbalanced datasets.

OBJECTIVE: Metamemory is integral for strategizing about memory intentions. This study investigated the prospective memory (PM) deficit in Parkinson's disease (PD) from a metamemory viewpoint, with the aim of examining whether metamemory deficits might contribute to PM deficits in PD. METHOD: Sixteen patients with PD and 16 healthy older adult controls completed a time-based PM task (initiating a key press at two specified times during an ongoing task), and an event-based PM task (initiating a key press in response to animal words during an ongoing task). To measure metamemory participants were asked to predict and postdict their memory performance before and after completing the tasks, as well as complete a self-report questionnaire regarding their everyday memory function. RESULTS: The PD group had no impairment, relative to controls, on the event-based task, but had prospective (initiating the key press) and retrospective (recalling the instructions) impairments on the time-based task. The PD group also had metamemory impairments on the time-based task; they were inaccurate at predicting their performance before doing the task but, became accurate when making postdictions. This suggests impaired metamemory knowledge but preserved metamemory monitoring. There were no group differences regarding PD patients' self-reported PM performance on the questionnaire. CONCLUSIONS: These results reinforce previous findings that PM impairments in PD are dependent on task type. Several accounts of PM failures in time-based tasks are presented, in particular, ways in which mnemonic and metacognitive deficits may contribute to the difficulties observed on the time-based task.

In the last decades, computational models have become useful tools for studying biological neural networks. These models are typically constrained by either behavioural data from neuropsychological studies or by biological data from neuroscience. One model of the latter kind is the Bayesian Confidence Propagating Neural Network (BCPNN) - an attractor network with a Bayesian learning rule which has been proposed as a model for various types of memory. In this thesis, I have further studied the potential of the BCPNN in short-term sequential memory. More specifically, I have investigated if the network can be used to qualitatively replicate behaviours of immediate verbal serial recall, and thereby offer insight into the network-level mechanisms which give rise to these behaviours. The simulations showed that the model was able to reproduce various benchmark effects such as the word length and irrelevant speech effects. It could also simulate the bow shaped positional accuracy curve as well as some backward recall if the to-be recalled sequence was short enough. Finally, the model showed some ability to handle sequences with repeated patterns. However, the current model architecture was not sufficient for simulating the effects of rhythm such as temporally grouping the inputs or stressing a specific element in the sequence. Overall, even though the model is not complete, it showed promising results as a tool for investigating biological memory and it could explain various benchmark behaviours in immediate serial recall through neuroscientifically inspired learning rules and architecture. / Under de senaste årtionden har datorsimulationer blivit ett allt mer populärt verktyg för att undersöka biologiska neurala nätverk. Dessa modeller är vanligtvis inspirerade av antingen beteendedata från neuropsykologiska studier eller av biologisk data från neurovetenskapen. En modell av den senare typen är ett Bayesian Confidence Propagating Neural Network (BCPNN) - ett autoassociativt nätverk med en Bayesiansk inlärningsregel, vilket tidigare har använts för att modellera flera typer av minne. I det här examensarbetet har jag vidare undersökt om nätverket kan användas som en modell för sekventiellt korttidsminne genom att undersöka dess förmåga att replikera beteenden inom verbalt sekventiellt korttidsminne. Experimenten visade att modellen kunde simulera ett flertal viktiga nyckeleffekter såsom the word length effect och the irrelevant speech effect. Däröver kunde modellen även simulera den bågformade kurvan som beskriver andelen lyckade repetitioner som en funktion av position, och den kunde dessutom repetera korta sekvenser baklänges. Modellen visade också på viss förmåga att hantera sekvenser där ett element återkom senare i sekvensen. Den nuvarande modellen var däremot inte tillräcklig för att simulera effekterna som tillkommer av rytm, såsom temporär gruppering eller en betoning på specifika element i sekvensen. I sin helhet ser modellen däremot lovande ut, även om den inte är fullständig i sin nuvarande form, då den kunde simulera ett flertal viktiga nyckeleffekter och förklara dessa med hjälp av neurovetenskapligt inspirerade inlärningsregler.

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The survival rate for a suddent cardiac arrest (SCA) is incredibly low, with less than one in ten surviving; most SCAs occur outside of a hospital setting. There is a need to develop an effective and efficient system that can sense, communicate and remediate potential SCA situations on a near real-time basis. This research presents a novel Zeolite-PDMS-based optically unobtrusive flexible dry electrodes for biosignal acquisition from various subjects while at rest and in motion. Two zeolite crystals (4A and 13X) are used to fabricate the electrodes. Three different sizes and two different filler concentrations are compared to identify the better performing electrode suited for electrocardiogram (ECG) data acquisition. A low-power, low-noise amplifier with chopper modulation is designed and implemented using the standard 180nm CMOS process. A commercial off-the-shelf (COTS) based wireless system is designed for transmitting ECG signals. Further, this dissertation provides a framework for Machine Learning Classification algorithms on large, open-source Arrhythmia and SCA datasets. Supervised models with features as the input data and deep learning models with raw ECG as input are compared using different methods. The machine learning tool classifies the datasets within a few minutes, saving time and effort for the physicians. The experimental results show promising progress towards advancing the development of a wireless ECG recording system combined with efficient machine learning models that can positively impact SCA outcomes.

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