Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte

Active Learning for Reducing Labeling Effort in Text Classification Tasks

Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERT$_{base}$ as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERT$_{base}$ outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice

Modeling Analytical Streams for Social Business Intelligence

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector

An analysis of students’ behaviour in a Learning Management System through Process Mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe exponential growth and transformation of the Internet and information technology in recent years led to the development of several analytical tools. As is the case with process mining, it emerged to fulfill the need to extract and analyze information from event logs by representing it in the form of process models. Process mining is an acclaimed tool and proved crucial in several areas, from healthcare to manufacturing and finance. Nevertheless, and despite the crucial role of digital systems in supporting learning activities and generating large amounts of data about learning processes, limited research focused on process mining applied to the educational context. Therefore, the aim of this dissertation is to apply a process-oriented approach and demonstrate the applicability of process mining techniques to explore and analyze students’ behavior and interaction patterns, based on data collected from Moodle, the widely used Learning Management System. We cover definitions of process mining, education, and a detailed search of the existing literature on educational process mining during this work. Furthermore, the paper analyzes and discusses the findings of the study that combines process mining techniques, specifically process discovery implanted in the Disco tool, with cluster analysis. Through the application of these two techniques, it was possible to recognize the relationship between the students’ behavior registered in the process models and the success of the students in the course, along with the general and specific information about the students’ learning paths. Besides, we obtained findings that allow us to predict the group of students at risk of failing. Finally, with the analysis of these results, we were able to provide improvement proposals and recommendations to enhance the learning experience

Occasional Groups in Crowdsourcing Platforms

Contributors to online crowdsourcing systems generally work independently on pieces of the product but in some cases, task interdependencies may require collaboration to develop a final product. These collaborations though take a distinctive form because of the nature of crowdsourced work. Collaboration may be implicit instead of explicit. Individuals engaged in a group conversation may not stay with the group for long, i.e., the group is an ``occasional group.\u27\u27 Occasional group interactions are often not well supported by systems, as they are not designed for team work. This dissertation examines the characteristics and work of occasional groups in the Gravity Spy citizen science project. Occasional groups in this system form to reach agreement about the description of novel categories of data that volunteers identify in the system. The author first employed virtual ethnography over six months to investigate volunteers\u27 interactions and to identify features of the occasional groups in this setting. Most groups were transient, interacting only for a short time to develop one product, but a few worked together repeatedly.To describe the overall process of finding new categories brings individuals to work together, the author interviewed nine active volunteers about their work practices. Volunteers individually or collectively use tools such as hashtags, collections and a search tool to identify examples of a new category and to agree on a name and description. Finally, the author investigated the details of the processes of developing proposals for four new categories over three years. She employed virtual and trace ethnography to collect messages from several discussion threads and boards to identify the analytical moves made by occasional group members in developing a new category. Volunteers would speculate on a new pattern and its causes, discuss how different categories are related and split or merge descriptions. They employed techniques such as detailed descriptions of data to create common ground, @-mention of other volunteers to increase the visibility of their work to each other and use of the category proposal as a vehicle to coordinate their actions. Findings contribute to the group literature by recognizing that groups with no formal formation and work processes are capable of doing work that would not otherwise be possible. The results advance our understanding of group categorization literature by showing how the analytical moves are different when group members work occasionally. The thesis also provides some suggestions for better support of occasional groups in crowdsourcing platforms

New measurement paradigms

This collection of New Measurement Paradigms papers represents a snapshot of the variety of measurement methods in use at the time of writing across several projects funded by the National Science Foundation (US) through its REESE and DR K–12 programs. All of the projects are developing and testing intelligent learning environments that seek to carefully measure and promote student learning, and the purpose of this collection of papers is to describe and illustrate the use of several measurement methods employed to achieve this. The papers are deliberately short because they are designed to introduce the methods in use and not to be a textbook chapter on each method. The New Measurement Paradigms collection is designed to serve as a reference point for researchers who are working in projects that are creating e-learning environments in which there is a need to make judgments about students’ levels of knowledge and skills, or for those interested in this but who have not yet delved into these methods