Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

"Tricky to get your head around": Information work of people managing chronic kidney disease in the UK

People diagnosed with a chronic health condition have many information needs which healthcare providers, patient groups, and resource designers seek to support. However, as a disease progresses, knowing when, how, and for what purposes patients want to interact with and construct personal meaning from health-related information is still unclear. This paper presents findings regarding the information work of chronic kidney disease patients. We conducted semi-structured interviews with 13 patients and 6 clinicians, and observations at 9 patient group events. We used the stages of the information journey – recognizing need, seeking, interpreting, and using information – to frame our data analysis. We identified two distinct but often overlapping information work phases, ‘Learning’ and ‘Living With’ a chronic condition to show how patient information work activities shift over time. We also describe social and individual factors influencing information work, and discuss technology design opportunities including customized education and collaboration tools

Biclustering fMRI time series

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Biclustering é um método de análise que procura gerar clusters tendo em conta simultaneamente as linhas e as colunas de uma matriz de dados. Este método tem sido vastamente explorado em análise de dados genéticos. Apesar de diversos estudos reconhecerem as capacidades deste método de análise em outras áreas de investigação, as últimas duas décadas tem sido marcadas por um número elevado de estudos aplicados em dados genéticos e pela ausência de uma linha de investigação que explore as capacidades de biclustering fora desta área tradicional Esta tese segue pistas que sugerem potencial no uso de biclustering em dados de natureza espaço-temporal. Considerando o contexto particular das neurociências, esta tese explora as capacidades dos algoritmos de biclustering em extrair conhecimento das séries temporais geradas por técnicas de imagem por ressonância magnética funcional (fMRI). Eta tese propõe uma metodologia para avaliar a capacidade de algoritmos de biclustering em estudar dados fMRI, considerando tanto dados sintéticos como dados reais. Para avaliar estes algoritmos, usamos métricas de avaliação interna. Os nossos resultados discutem o uso de diversas estratégias de busca, revelando a superioridade de estratégias exaustivos para obter os biclusters mais homogéneos. No entanto, o elevado custo computacional de estratégias exaustivas ainda são um desafio e é necessário pesquisa adicional para a busca eficiente de biclusters no contexto de análise de dados fMRI. Propomos adicionalmente uma nova metodologia de análise de biclusters baseada em algoritmos de descoberta de padrões para determinar os padrões mais frequentes presentes nas soluções de biclustering geradas. Um bicluster não é mais que um hipervértice num hipergrafo . Extrair padrões frequentes numa solução de biclustering implica extrair os hipervértices mais significativos. Numa primeira abordagem, isto permite entender relações entre regiões do cérebro e traçar perfis temporais que métodos tradicionais de estudos de correlação não são capazes de detetar. Adicionalmente, o processo de gerar os biclusters permite filtrar ligações pouco interessantes, permitindo potencialmente gerar hipergrafos de forma eficiente. A questão final é o que podemos fazer com este conhecimento. Conhecer a relação entre regiões do cérebro é o objetivo central das neurociências. Entender as ligações entre regiões do cérebro para vários sujeitos permitem traçar perfis. Nesse caso, propomos uma metodologia para extrapolar biclusters para dados tridimensionais e efetuar triclustering. Adicionalmente, entender a ligação entre zonas cerebrais permite identificar doenças como a esquizofrenia, demência ou o Alzheimer. Este trabalho aponta caminhos para o uso de biclustering na análise de dados espaço-temporais, em particular em neurociências. A metodologia de avaliação proposta mostra evidências da eficácia do biclustering para encontrar padrões locais em dados de fMRI, embora mais trabalhos sejam necessários em relação à escalabilidade para promover a aplicação em cenários reais.The effectiveness of biclustering, simultaneous clustering of both rows and columns in a data matrix, has been primarily shown in gene expression data analysis. Furthermore, several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades witnessed many biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Following hints that suggest potentialities for biclustering on Spatiotemporal data, particularly in neurosciences, this thesis explores biclustering’s capacity to extract knowledge from fMRI time series. This thesis proposes a methodology to evaluate biclustering algorithms’ feasibility to study the fMRI signal, considering both synthetic and realworld fMRI datasets. In the absence of ground truth to compare bicluster solutions with a reference one, we used internal valuation metrics. Results discussing the use of different search strategies showed the superiority of exhaustive approaches, obtaining the most homogeneous biclusters. However, their high computational cost is still a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. We propose a new methodology for analyzing biclusters based on performing pattern mining algorithms to determine the most frequent patterns present in the generated biclustering solutions. A bicluster is nothing more than a hyperlink in a hypergraph. Extracting frequent patterns in a biclustering solution implies extracting the most significant hyperlinks. In a first approach, this allows to understand relationships between regions of the brain and draw temporal profiles that traditional methods of correlation studies cannot detect. Additionally, the process of generating biclusters allows filtering uninteresting links, potentially allowing to generate hypergraphs efficiently. The final question is, what can we do with this knowledge. Knowing the relationship between brain regions is the central objective of neurosciences. Understanding the connections between regions of the brain for various subjects allows one to draw profiles. In this case, we propose a methodology to extrapolate biclusters to threedimensional data and perform triclustering. Additionally, understanding the link between brain zones allows identifying diseases like schizophrenia, dementia, or Alzheimer’s. This work pinpoints avenues for the use of biclustering in Spatiotemporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of biclustering’s effectiveness in finding local fMRI data patterns, although further work is needed regarding scalability to promote the application in real scenarios

Heterogeneous integration of InP etched facet lasers to silicon photonics by micro transfer printing

Photonics Integrated Circuits allow optical functionalities and interconnects with small footprint, large band -width and -density, low heat generation. The silicon photonics platform (SOI) offers excellent waveguiding properties, large-area wafers and a highly developed CMOS infrastructure matured with electronics. Nevertheless, the key function of light amplification is missing due to the indirect band-gap of silicon. The light has to be provided to the SOI from a separate direct band-gap III-V material. InP based devices work in the infrared optical window of the electromagnetic spectrum and can be heterogeneously integrated to the SOI. This research deals with the development of the first stand-alone InP Fabry-Perot lasers heterogeneously integrated to SOI by Micro Transfer Printing (µTP). The lasers are pre-fabricated and tested before transfer and are optimized to reach excellent optical, electrical and thermal performance. Lasers printed on Si substrates emit over 20 mW optical power, have threshold current of 16 mA and series resistance of 6 Ω; the thermal impedance of 38 K/W is half of that for the same laser printed directly on the SOI. The transfer printable InP ridge lasers have been designed as rectangular coupons with both contacts at the top and etched facets at the sidewalls. Two main release technologies based on the FeCl3:H2O (1:2) solution and a InGaAs or a InAlAs sacrificial layer were developed for releasing the devices from the original InP substrate with selectivity to InP greater than 4000 at 1 ◦C. The working principle of a polymer anchor system which restrains the devices to the substrate during the undercut were determined. The devices were printed on different silicon photonic substrates with excellent adhesion, with and without adhesive layers. A process for creating recesses into the SOI was developed to allow edge coupling the laser waveguide to the SOI or a polymer waveguide. High alignment accuracy along the three spatial directions can be achieved with alignment markers, reference walls and the interposition of a metal layer beneath the devices. This work shows a possible path for the achievement of a laser source for silicon photonics and it has been the basis for the integration of others InP devices to PICs by micro transfer printing

Army Hand Signal Recognition System using Smartwatch Sensors

The organized armies of the world all have their own hand signal systems to deliver commands and messages between combatants during operations such as search, reconnaissance, and infiltration. For instance, to command a troop to stop, a commander would lift his/her fist next to the his/her face height. When the operation is carried out by a small unit, the hand signal system plays a very important role. However, obviously, there is an aspect of limitation in this method; each signal should be relayed by individuals, which while waiting attentively for a signal can cause soldiers to lose attention on the front observation and be distracted. Another limitation is, it takes a certain period to convey signals from the first person to the last person. While the limitations above are related to a short moment, that can be fatal in the field of battle. Gesture recognition has emerged as a very important and effective way for interaction between human and computer (HCI). An application of inertial measurement unit (IMU) sensor data from smart devices has lead gesture recognition into the next level, because it means people don’t need to rely on any external equipment, such as a camera to read movements. Especially wearable devices can be more adequate for gesture recognition than hand-held devices because of its distinguished strengths. If soldiers can deliver signals using an off-the-shelf smartwatch, without additional training, it can resolve many drawbacks of the current hand signal system. In the battlefield, cameras to record combatants’ movement for image processing cannot be installed nor utilized, and there are countless obstacles, such as tree branches, trunks, or valleys that hinder the camera to observe movements of the combatants. Because of unique characteristics of battlefield, a gesture recognition system using a smartwatch can be the most appropriate solution for making troops mobility more efficient and secure. For the system to be used successfully in combat zone, the system requires high precision and prompt processing; although accuracy and operating speed are inversely proportional in most of cases. This paper will present a gesture recognition tool for army hand signals with high accuracy and fast processing speed. It is expected that the army hand signal recognition system (AHSR) will assist small units to carry-out their maneuver with higher efficiency

Investigating and Supporting Sensemaking within Online Health Communities

This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Proceedings, MSVSCC 2017

Proceedings of the 11th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 20, 2017 at VMASC in Suffolk, Virginia. 211 pp